{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c47b0f4d",
   "metadata": {},
   "source": [
    "# Stablility & White Noise Recognition\n",
    "1. plot time-series graph\n",
    "2. use ADF for stability test\n",
    "3. use $Q_{LB}$ for white noise test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2592f1bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CPI in QingHai province\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import warnings\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import ticker\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "    \n",
    "warnings.filterwarnings(\"ignore\")\n",
    "qhcpi = pd.read_csv('./data/cpi.csv', usecols=['Date','QHCPI'], index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44cd8406-cd9a-498c-bc99-bf110ead5a98",
   "metadata": {},
   "source": [
    "## plot time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53913aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7x/z589m8eXOq/UJCQpg/f36S3elEJPc6edIkn1q0gEaN4MQJeOEFyJfP6siyN5sN2rWDPXvMUr4RI6BxY5OsEhERERERcTVOJ6WWLFnCmDFjCA8PT7VfZGQkY8aMYenSpc5eSkRyiLAweP55s4te2bJw/Lipl6QC3mnj5gZPPQWHD0O/fma5Y0AA/PST1ZGJiIiIiIg4zumk1Ndff02+fPno2LFjqv06dOhAvnz5+Oqrr5y9lIi4uIgIU8C8ShW4dQt++QWmT4dixayOzLXlzQsDB5qZZq1aQZs2ZhnksWNWRyYiIiIiInJ3Tielzpw5g6+vL3nypF4rPW/evPj6+vLbb785eykRcVFXr8Kbb0KlSmbJ3u7dsHAhlCtndWQ5S4ECMHq0SU5VqgQPPWR26Tt3zurIREREREREUuZ0Uip+ez9HFC5cmIiICGcvJSIu5uZNePddqFwZtmyBb76BFSvA39/qyHK2IkVgyhQ4csTMoqpe3SSr/vrL6shERERERESScjopVaxYMU6dOnXXfna7nVOnTuHj4+PspUQkG4mNNYmmtWu92LLFfB0vLg4++QSqVoWlS82/g4OhXj2ros2dypSB//s/U2Pq/HmTHJw8Ga5cSdwv/nf5ySck+V2KiIiIiIhkNqeTUg888ADh4eF88cUXqfZbtWoVly5dop5elYq4vKAgqFABWrd2Y9iwErRu7UaFCvDFF7Bhg0k+TZ4Mb78N27dD8+ZWR5y7VaoEy5aZ3fl27jTJqXnz4MaNf36XLVvC00+bzxUqmHYREREREZGs4HRSqk+fPtjtdl588UU2bdqUbJ+QkBCGDh2KzWajT58+TgcpItYLCoJu3ZLWKTp3zrT36wcjRsD+/dCxI9hsloQpyahVC9atM8nDTz+F8uWha9ekv8vQUPO7VGJKRERERESyQupVylPx2GOP0alTJ9asWUPnzp2pV68eDRs2xMfHh4iICHbu3MlPP/2E3W6nQ4cOd92lT0Syr9hYGDYM7PaU+3h4mBk37u5ZF5ekzcMPw+bNZnlfcux2k0wcPhw6ddLvUkREREREMpfTSSmAxYsXM3z4cJYtW8aePXv46aefEo7Z7XZsNhu9e/dm1qxZ6Q5URKyzbdvdd3I7d870a9EiS0ISJ333HVy8mPJxux3OntXvUkREREREMl+6klIeHh4sWLCAoUOHsnr1an7++WeioqLw9vamRo0adOrUiWrVqmVUrCJikbCwjO0n1tHvUkREREREsot0JaXiVatWTcknkRwspeVezvYT6+h3KSIiIiIi2YXThc5FJPeoUgXy5Uv5uM0Gvr7QtGnWxSTOadrUFDpPqRC9fpciIiIiIpJVlJQSkVT9+qtJULRqZRIWdyYz4r+ePVuFsV2BuzvMmWP+nVxiym6HN9/U71JERERERDJfupJSV65cYdq0aTRt2pSyZcvi7e1NoUKFkv3w9vbOqJhFJIv88AM0bgzPPQcbNsDKlVCuXOI+5cub9sBAa2KUtAsMTP536esLDz0E69alvtOiiIiIiIhIRnC6ptSFCxdo27YtJ06cwO7AqxdH+ohI9rFuHfTpA/PnQ48epi0wEDp1gq1b4zh06C9q1ChG8+ZumlXjguJ/l9u2maLmZcqYGXHR0dCoEcycCaNGWR2liIiIiIjkZE4npSZNmsTx48cpWbIkL730Eg0aNKBkyZK4uWlFoIirW7gQxo+HoCBo2TLxMXd3aNECKlaMxs+vGPov77rif5e38/aGNWugYUOoWRMCAiwJTUREREREcgGnk1IbN27E3d2dVatWUbt27YyMSUQsYrfDxImwdCls3Qo1algdkVihcmVYvhx69oTvv4f77rM6IhERERERyYmcnuNw4cIF7r33XiWkRHKIGzegb19Yvx527lRCKrd79FEYM8Ys8YuKsjoaERERERHJiZxOShUvXhwvL6+MjEVELBIZCY89BufPw//+l7QAtuROI0dCnTrQuzfExVkdjYiIiIiI5DROJ6XatGnDkSNHiIiIyMBwRCSrhYZCs2am0PWGDVC4sNURSXZhs8F778HZs/D661ZHIyIiIiIiOY3TSanx48dToEABRowYQWxsbEbGJCJZ5NAhs9Nahw6mjlS+fFZHJNlNgQKwerUpfh8UZHU0IiIiIiKSkzhd6PzUqVOMHz+e8ePHs2/fPvr37899992X6pK+Jk2aOHs5EclgW7ZA164weTIMGmR1NJKd+fnB55+b+lL33Wd25RMREREREUkvp5NSAQEB2Gw2AI4ePcq4ceNS7W+z2YhStVyRbOGTT2DIEDM76vHHrY5GXEHTpiaB2bkz7N4NRYtaHZGIiIiIiLg6p5NSvr6+CUkpEXENdjvMnGk+vvoK6te3OiJxJYMGwd690L07BAdDHqf/goiIiIiIiKQjKXX48OGMjENEMllsLAwfDhs3wvbtULmy1RGJq7HZYN48aNkSxoyBt96yOiIRcUWxsbBtG4SFmU02mjYFd3eroxIREREr6H1ukVzg2jV4+mnzAmDHDihRwuqIxFXlywdffAEPPgh16kDv3lZHJCKuJCgIhg2Dc+f+aStfHubMgcBA6+ISERERazi9+56IuIaLF6F1a4iLg5AQJaQk/UqXhlWr4MUX4ccfrY5GRFxFUBB065Y4IQUQGmratcOniIhI7qOklEgOdvIkNG5sZrQEBUGBAlZHJDnFQw/B3Lmm8Pnvv1sdjYhkd7GxZoaU3Z70WHzb8OGmn4iIiOQeDi3fa9euHQB+fn4sXLgwUZujbDYbGzZsSGN4IuKs3bvNznovvQSjR5t6QCIZqXdvU/i8WzczCy9fPqsjEpHsatu2pDOkbme3w9mzpl+LFlkWloiIiFjMoaTUtm3bALj//vuTtDlKO/WJZJ31603CYN486NnT6mgkJ5s+Hdq1g6FD4e/3LEREkggLc6zfsGHQti3UrAm1akHVquDhkbmxiYiIiHUcSkq9++67ABQuXDhJm4hkLwsXwvjxphh1q1ZWRyM5XZ48sGKFWc737rswaJDVEYlIduTl5Vi/Tp1MDcSVK+HVV83sqvvuM0mq+ERVzZpwzz3gpiIUIiIiLs+hpFSvXr0cassse/fuZdOmTezZs4fdu3cTFhaGh4cHly5dSvVxy5cvZ+HChRw5coR8+fLx0EMPMWbMGBo2bJik79GjR1m/fj2bNm3i119/5c8//8THx4eGDRvywgsv8PDDDzsVe3BwMLNnz+bAgQPY7XZq167N8OHD07z8UeRu7Hb497/hww9hyxZz0y6SFYoVgzVroFkzqF7dbO8uIhLv229h8GCTmLp6Nfm6Ujab2YXv1VfB3f2f9qgo+PlnOHjQfLz1lvl88ybUqJE4UVWzJhQtmnXfl4iIiKSfQ0kpq02bNo3169en6TFjxoxh3rx5eHp60rp1a65fv05ISAibNm1i2bJldOzYMVH/Dh06cP78eby9vXnwwQdp0KABhw8fZu3ataxbt45p06bxwgsvpCmG+fPnM2rUKPLkyUPLli3x8PBg06ZNdOvWjRkzZjBkyJA0nU8kJTduwLPPwk8/wfffmxt7kaxUsya89x488YSpZ+bra3VEImK1mBgzc3fp0n+W93brZhJQtyem4is8zJ6dOCEF4O0NjRqZj3h2O5w//0+iatcu8/xz+DAUL540UaUlgCIiItmXLTo6Opn3q+7unXfeoXv37pQsWTKjY0rirbfe4tq1azzwwAPUq1ePihUrpjpTasuWLbRv355ixYoREhJC5cqVAdi1axcBAQF4enry888/U6RIkYTHPP744/Tu3ZvOnTuT77Zqve+//z4vvvgi7u7u7Nq1i6pVqzoU8/Hjx3nwwQdxd3cnODiYBg0aJLS3bt2ayMhI9uzZkxCbI6KioihTpgyRkZF4e3s7/DjJ2aKioGtXs9whKAhuW2WbaeLi4jhz5gx+fn64af2E3OaVV+DLL02x4uyy26PGq7iSnDJeDx0yNQ1LlYIlS6BsWdMeFGTqRt1e9NzX1ySkAgPTd82bN+H4cZOoOnDgn6RV/BLA2xNVWgKYMXLKeJXcQeNVXElOGK9RUVEULlyYsLCwVPMXTn9348aNw9/fnyeeeII1a9Zw8+ZNZ091VyNGjGDixIm0a9eOUqVK3bX/3LlzARg9enSipE+DBg0YMGAAkZGRLF26NNFj1q1bx5NPPpkoIQUwYMAAWrduTWxsLKtWrXI45vnz53Pr1i0GDBiQkJACuO+++xg1ahS3bt1iwYIFDp9PJDmhoWapVKlSEBycNQkpkdS89pqZqffss8kv0RGRnC0uDubMgSZNoH9/2Ljxn4QUmMTT6dOweTN8/LH5fOpU+hNSAHnzQrVq0L07TJ4Ma9eac//1F7z/PrRsaQquv/UWPPCA+ZvZqBEMHGg2Btm6Fe5SGcJhsbFmKf0nn5jPsbEZc17JevpdiohkLqeTUnXq1OHWrVsEBwfTq1cvKlasyMsvv8yePXsyMr40u379Olu2bAGgS5cuSY7HtwUHBzt8zpp/F+cJc3TrmNvOn1wMgX/feW3YsMHh84nc6eefzc10+/bw0UdwRz5VxBJubmY87t1rXviJSO4RFgaPPWYSQNu2wfDhyc9EcneHFi2gRw/z+c4lexktfgng7cmnv/6CI0dMDatKlcwSwGHDoEwZKFfO7Co6erR5Ptu3zyxFdFRQEFSoYJJgTz9tPleoYNrFteh3KSKS+ZxOSn333Xf8+OOPDB8+nDJlyhAeHs6iRYto0aIFDz74ILNmzUpTEiejHD16lJiYGIoXL065cuWSHK9Tpw4Ahw4dcvicp0+fBnBolhZAREQEZ8+eBaB27dpJjpcrV47ixYtz5swZIiMjHY5DJN6WLdC8OYwbB1Om/FOPQyQ78PaG1ath6lT46iuroxGRrLB6NdSubWYq/fBD9t9sw2YzyaeAgMTJpytX4JtvoG9fM/Pq88+hSxcoWNBs5PDUU4lnYcXFJT5vUJCpm3X78kQwM5u7dVMyw5XodykikjXStTixSpUqTJo0iaNHj7JmzRq6deuGp6cnR44c4ZVXXqFKlSp07tyZlStXEpOWt5jS4dzffzmSS0gBeHl54ePjQ3h4OJcvX77r+U6ePJkw66l9+/YOxRCfkCpSpAheKeyBXPbvuezxfUUctWKFuUFevNjsZiSSHfn7w/LlpqbMiRNWRyMimeXKFbNc9/nnzf/5t9+G/Pmtjsp58UsA70w+/fUXLFqUdAmgjw80bmxmYc2dC4MGJb90Ob5t+HAt/3IFsbFm5px+lyIimS9Ddt+z2Wy0bt2a1q1bc+XKFb744gs++eQTtm/fzrfffsumTZsoXLhwQsIoM125cgWAAqlU2C1QoAARERFcuXKFQoUKpdjv1q1bDBw4kJiYGLp160bdunUdiiE6OhoAT0/PFPvEJ6vi+yYnJiYmUTIvPokWFxdH3J1vzUmOZ7ebm/2ZM21s2GCnQYOk79BmlfgxqHEoqWnbFkaNgk6dbOzYYSeVp9tMpfEqrsSVxusPP0Dv3jZq1YJ9++wUK2bd36XMVrAgNGxoPuLZ7WbWzMGDprD7unU2LlxIeeqy3Q5nz8LWrXG0aJH5MWcFVxqvabF1K5w7l/J79znxd5kb5NTxKjlTThivjsaeIUmp2xUsWJC+ffvSt29fzpw5w8iRI9mwYUOWLVOz//32hS2V9Ux2B6vvjhgxgh07dnDvvfcya9asLI9h5syZTJkyJUn72bNnU02mSc4TGwv/+U9Rtmzx5NNP/6BMmVucOWNdPHa7nfDwcGw2W6rjXOTJJ2HHjuI8+aSNBQsuWLLTlcaruBJXGK+3bsH8+YV5/31vJk68RLduV4iOhlTeZ8vRqlc3H56eXnz7bYm79h858gatWl3j/vtvULXqTcqVu+Wyy/BdYbzezbVrNo4dy8vRo/k4etR83r/fw6HHHjr0FxUr5tKB74JywniV3CMnjFdHVqZBJiSlwBQEX7FiBcuXL+fo0aOZcYkUxSdrUpuBdO3aNcAk0FIydepU3nvvPUqWLMmaNWsoWrSowzHEn/fq1asp9ok/ltLyPoCRI0cydOjQhK8vX76Mv78/vr6+qW6pKDnLtWvQq5eN8+dh1y47JUqUvfuDMllcXBx2ux1fX1+X3aJUss7y5dCsmY0PP7yHV1/N+i35NF7FlWT38XrqFPTtayMuDn780U6lSkUBx++RcrIaNRzr17x5Pq5d82DZMjhwwLzxVKOG+ahZ0/73Z0jDradlsvt4vV1sLPz6a/zMNlvCDLdTp+Dee0n4uQcE2Ll2zdQVu5saNYrh51cs84OXDOFK41UkJ4zXqKgoh/plWFLq+vXrrFmzho8//pgtW7Yk/BALFChAx44d6dmzZ0ZdKlXly5cHIDQ0NNnj0dHRRERE4OPjk+Jso4ULFzJp0iQKFy7M6tWrqVSpUppi8PX1BSA8PJzo6OhkE0/nz59P1Dc5Hh4eeHgkfafGzc3NZQempM3Fi9CxIxQvbrbNLlAg+2TJ48ehxqLcTcGCsGoV1K8PderYSGZT0kyn8SquJDuOV7sdli6Fl14ytXYmTIA8ebLP36TsoHlzKF/eLOlLbkK8zWaOz5zplrDj4O1LAA8ehB9+sPHee2ZnwBIlTJLk9o+qVSGZW0NLZcfx+uefJuEX/3M9eNDsWlywINSqZX6WHTqYDWOqVYPEt+o2YmPNGL/b77J5czdLZgCL87LjeBVJiauPV0fjTndS6rvvvuPjjz9m9erVXL58Gbvdjs1m4+GHH+bpp58mMDAw1RlJGc3f3x8PDw8uXrxIaGhokoLn+/btA6BGCm9nrVixghEjRlCgQAG++OKLZHfPuxsfHx98fX05e/Ys+/fvp3HjxomOh4aGcvHiRXx9fSlcuHCazy+5w8mTZkvq1q1N8dQ8mTKvUSRr3HMPfPaZKdLv72+WuoiIa7h0yRTw3rMHvvwSGjWyOqLsyd0d5swxO7PZbImTGfErL2bPJiEhFd9evrz5aNfun/abN+HYsX8SKp99Bv/+t0mS+PsnTlTVqmWeY110dUe6XL1qkk23J58OHoSoqH9mPtWsCU8/bT47uJG2U79LERFxjtMvcydPnszHH3/MmTNnEuojVahQgaeffpqnn36aChUqZFSMaeLp6Unz5s35+uuvWbVqFS+88EKi46tWrQIgICAgyWM3btzIwIEDyZs3L5988gmN0nHXFRAQwKJFi1i1alWSpFTQ33vItrv97kPkNj/+aN7BGz4cxozJnTeakvM0bw7/+Q906mQKJLvC0hSR3C4kxCxjatMG9u3Dsg0LXEVgIKxcaWaT3b6/T/nyJokRGOjYefLm/adW1VNP/dMeFWWWnB08aGYCffWV+Xf8EsA7Z1bllOfZ25fe3f5x6hRUqPDP7KfBg83nSpXSnzBK6XdZrpxJWDn6uxQRkdTZoqOjnSrwET/7qVChQnTu3JmePXvSpEmTDA0uJV5eXnh4eHDp0qVkj4eEhPD4449TrFgxQkJCqFy5MgC7du2iXbt2eHh48PPPPyeqE/X999/z+OOPc/PmTZYtW8bjjz/uUCzxO/J9+eWXlC37T62fY8eO8eCDD5InTx42btxI/fr1AThx4gStWrUiIiKCH3/8EX9/f4e/76ioKMqUKUNkZKRqSuVgGzZAr15mdlSvXlZHk7y4uDjOnDmDn5+fy04nFWvY7Wb7+LNnzYyLrJgBqPEqriS7jNeYGLN86cMP4d13oWtXy0JxSbGxsG0bhIVBmTLQtGnmzaq5fQng7UvWMnsJYGys2X3u0KG/qFGjGM2bu2XI95ja0rv4mWHx30vSpXcZL/53ef48jB8P8+aZNw7F9WSX51cRR+SE8RoVFUXhwoUJCwtLNX/hdFKqffv29OrVi86dO+Pp6el0oI7YuHEj06ZNS/h69+7d2Gw2HnzwwYS2sWPHJpr9NGrUKObPn0+BAgVo1aoVN27cICQkhLi4OD766CM6d+6c6BrlypUjIiKCChUq8PDDDycbR+PGjenXr1+itvh6Ub/88gv33HNPomPvvPMOY8eOJU+ePLRq1Yp8+fKxadMmrl27xrRp0xIVMXeEklI536JFMHasmabfurXV0aQsJzxJinViYqBlS3j4YZgxI/Ovp/EqriQ7jNeff4aePU1CY8kSMzNEXE/8EsA7EzzJLQGsWdPMOHJ0ZnZQUPKzwdIygyi1pXfVqydNQDm69C4zzZwJu3fDp59aHYk4Izs8v4o4KieMV0eTUk6/R/3ll186+9A0u3DhArt3707UZrfbE7VduHAh0fEZM2ZQq1YtFi5cSEhICHnz5qVFixaMGTMmyXI6gIiICABOnz7N6dOnU4zlzqRUaoYOHUqlSpWYPXs2O3bsAMzMquHDh9O+fXuHzyM5n90Or74Kixebgua1alkdkUjm8fCAL76Ahx6COnXMi18RsZ7dbmaB/Pvf5m/SsGGoiLMLu30JYI8e/7THLwGMT1aldQlgUJCptXRnAfDQUNO+cmXixFRsrKmTeWdyLH7pXfx1MnLpXWbp1Qtee83UWcspSyNFRKzm9Eyp5MTExBAVFYW3t3eyu8ZJ+mimVM5086ZZzrRnDwQHm3cas7uckLkX6/3wAzz6KHz7LTzwQOZdR+NVXIlV4zUsDPr3N4mFjz82yQHJPeKXAN6ZOLpzCWD16qbW5R9/JH8em83sGDxmzD+zoG5fend7cfasWHqXGR5/HAIC4PnnrY5E0kr3A+JKcsJ4zfSZUvHOnDnD7Nmz2bhxI2fPnk1o9/Pzo127drz44ov4+fml9zIiOVJUlHlX8dYtU6/Ax8fqiESyTv36pvBv586muH92WJohkhutWWPeHOnZE1avhvz5rY5IstrtuwA+9tg/7TdvwtGj/ySp3n035YQUmOTWhQvwzTfQtq2ZoVWrVs56fu/fH6ZOVVJKRCSjpCvltnHjRho0aMCiRYsSduGL//jtt99YuHAhDRo0YOPGjRkVr0iOcf682Y2sRAkzQ0oJKcmN+vY1BZS7dYMbN6yORiR3iY6G556DIUNg+XKYNUsJKUksb16zpK9HD5gyBV580bHH9e0LL79sdm3MSQkpMEXOT582SyBFRCT9nE5KnTp1it69e3P58mV8fX2ZPn06GzZs4KeffmLDhg3MmDEDPz8/Ll++TJ8+fTh16lRGxi3i0n75BRo1MtO/P/ooY3bBEXFVM2dCvnymfo2IZI0ffoC6deGvv8ySrTZtrI5IXEGZMhnbzxXly2dmFX7wgdWRiIjkDE4npWbNmsW1a9fo2rUrBw4cYMiQITRr1gx/f3+aNWvG4MGD2b9/P127duXq1avMmTMnI+MWcVn/+x80a2bqLUydqiKyInnymB0nv/oKFi60OhqRnC02FiZNMvXcxo41RamLFbM6KnEVTZuaJX4p7dJns4Gvr+mXk/XrB8uWmeWNIiKSPk6/HN68eTOenp6888475MmTfGmqPHny8M477+Dp6cmmTZucDlIkp/jsM+jUCd5/3yyXEBGjWDFTy2bsWPjuO6ujEcmZTp0yy8bXrzfb2j/zTMrJBZHkuLtD/PvMd46d+K9nz86+u+dllDp1oGxZU35BRETSx+mk1Pnz56lSpcpdd4Hz9vamSpUqnD9/3tlLibg8ux3eegteeMHcwHTqZHVEItlPrVqwaJGpL3Xbvhkikk52Oyxdana5fOQRs7FG5cpWRyWuKjDQzLArVy5xe/nypj0w0Jq4slr//lrCJyKSEZzefc/Dw4Po6GiH+kZHR+OhojmSC8TGmpv9sDBTTyF++vrLL8OXX8L27XDffdbGKJKddesG+/ZBly7m/5Knp9URibi28HAYNMjscPnll6aeoUh6BQaaN9i2bo3j0KG/qFGjGM2bu+X4GVK3e/ppGD/e7DZYooTV0YiIuC6nZ0r5+/tz/PhxDh48mGq//fv3c+zYMfz9/Z29lIhLCAqCChWgZUtzo9KyJdxzDzz8MHz/PezYoYSUiCPeeMMkdZ97zszwEBHnbN5sZiB6eZlkrxJSkpHc3aFFC+jYMZoWLXL+kr07FS9uarMtX251JCIirs3ppFRgYCB2u52nn36anTt3Jttn586d9OzZE5vNRteuXZ0OUiS7CwoyMzzOnUvcHhoKu3bB8OFQsqQloYm4HDc3U0B2926zRb2IpE1MDIweDU8+af4PLV4MhQpZHZVIzhO/hE9voIiIOM/p5XsDBw7k448/5tChQ7Rp04aHHnqI6tWrU6pUKf744w9+/vlndu/ejd1up1atWjz33HMZGbdIthEba7ayT+mGxGYzxZu7d8997yKKOKtwYVizBho3hpo1tV29iKN++cVsV1+smJkddWfdHxHJOAEB8K9/mf9rdetaHY2IiGtKV02ptWvXMmDAADZv3swPP/zA7t27E47b/36F3rp1axYtWqSaUpJjbduWdIbU7ex2U7R52zYzzV1EHHP//fDRR9Cjh5lxWKmS1RGJZF92O/zf/8HEifDqq+bNEjen58OLiCPy5IHevc1sKSWlRESc43RSCqBkyZKsW7eOXbt2ERwczLFjx4iKisLb2xt/f3/atWtHgwYNMipWkWwpLCxj+4nIPx57DEaONAV1v/9eS5BEkvP772YZ0blz8L//mTpSIpI1+vWDZs1gxgzQe/AiImnndFJq/vz5AAwYMIAGDRoo+SS5VpkyGdtPRBIbM8Ysjejb12w3rtkfIv9Yu9YsH3r6aVi1CvLntzoikdylenUzk3f9elAJXRGRtHP61n7MmDEsXLhQy/Ik12vSBLy9Uz5us4GvLzRtmnUxieQkNhu8/z6cPAmTJlkdjUj2EB0NAwfCoEFmY4DZs5WQErFKfMFzERFJO6eTUsWKFcPHxycDQxFxPTdvwrPPmqLMNpv5uF3817Nnq8i5SHp4ecHq1TBvnimALpKb7d5t6tdcuAAHDkDbtlZHJJK7PfUUbNmiUg0iIs5wOilVr149Tp48SVxcXEbGI+IyLl+GDh3g9GnzomDlyqS7HJUvb9oDAy0JUSRHqVABPvvMvCP9889WRyOS9WJjYfJkk4QaMwa++AKKF7c6KhEpUsTcEy5bZnUkIiKux+mk1PPPP094eDjz5s3LyHhEXEJYmClqWbQobNwIPj4m8XT6NGzeDB9/bD6fOqWElEhGatEC3ngDOneG8HCroxHJWLGxZrbF2rVebNlivo53+rQZ/2vXwg8/wIABSWfnioh14pfw/b0BuYiIOMjpQuetW7dm+vTpTJw4kV9//ZW+fftStWpVPD09MzI+kWzn8GEICDBTtadOTVx02d3dvGgQkczz/POwd6/5P7hhg5bGSs4QFATDhsG5c25ACcDMtp09G65dgxdfhKFDYeJEyJvX0lBFJBmPPAJRUSZprP2fREQc53RSqtBt+3IvXryYxYsXp9rfZrMRFRXl7OVEsoVt26BLF3jtNXjhBaujEcmdbDaYP98kgMeNg+nTrY5IJH2CgqBbt6QzLEJDTXvJkmZnr8aNrYlPRO7O3R369DGzpZSUEhFxnNPL9+x2e5o+VHtKXN3nn0PHjrBokRJSIlbz8DD1dJYvN8tlRVxVbKyZIZXckp/4trx59SJXxBX06weffmpmN4qIiGOcnin1yy+/ZGQcItnarFlmqd6GDdCokdXRiAhA2bJmhklAAFSpAvXqWR2RSNpt2wbnzqXeJzTU9NPycJHszd8fqlc3u8X26GF1NCIirsHppJSfn19GxiGSLcXGwogRsG4dfPedudkQkeyjQQN4+21T+PzHH80yJxFX4ugW8tpqXsQ19OtnlvApKSUi4hinklKHDh3ixIkTANx3331Ur149Q4MSyQ6uXYPeveHMGfj+e73YFcmu+veHfftM7Z1vv4V8+ayOSMRxZcpkbD8RsdaTT8JLL8HZs+Dra3U0IiLZX5pqSh0+fJjGjRvTqFEjevfuTe/evWnYsCEPP/wwR44cyawYRbLcX39BmzYQEwObNyshJZLdzZwJefLA8OFWRyKSNk2bml32UmKzmRe2TZtmXUwi4jxvb7MpztKlVkciIuIaHE5K/fXXXzz22GMcPHgwSRHz/fv30759ey5dupSZsYpkiVOn4OGHTU2AVavAy8vqiETkbvLmhc8+g+BgsxmBiKtwd4dXXkn+mM1mPs+ebfqJiGvo1w+WLEl+AwMREUnM4aTUu+++y4ULF/Dy8mLSpEls27aN//3vf7zxxhsULFiQP//8kwULFmRmrCKZbs8es+V2nz7w7rtm5oWIuIbixU1x2dGjYft2q6MRccy1a/Df/0JgYNIZU+XLw8qV5piIuI4WLeDWLf0tEhFxhMMvub/++mtsNhvvv/8+7du3T2ivV68e999/P927d2fjxo1MmDAhUwIVyWzBwdCzp3lHuk8fq6MREWfUrg0LF5r6Urt3mzo8W7fCoUNe1KgBzZtrxolkH3Y7DBwIpUrB55+br7dujePQob+oUaMYzZu7abyKuCA3N+jb1xQ8b9LE6mhExNXExuau+1eHk1InTpygRIkSiRJS8dq3b0+JEiU4efJkhgYnklXefx9GjYJPPzW1pETEdT35JOzfb96pvn4dQkPdgBKAmXkyZ45mnkj2MHs2/PAD7NplXsSCGbcVK0bj51csoU1EXE/fvuaNkrlzVQpCRBwXFATDhsG5c7nn/tXh252oqCgqVKiQ4vEKFSpw+fLljIhJJMvY7fDaa6aex+bNSkiJ5BR168Kvv0JoaOL20FAziyooyJq4ROJ9+y1MmgRr1kDhwlZHIyIZ7d574YEH4IsvrI5ERFxFUJC5Tz13LnF7Tr9/dTgpZbfbyZNKgZ08efJgVzU/cSE3b8KAAWbJxPffm3ezRMT1xcaa7biTE/9navhw00/ECidPQo8eZneu+++3OhoRySz9+5slfCIidxMba2ZIJZdSyen3r5oYLrnS5cvw+OPmhcF334Gfn9URiUhG2bYt6TtMt7Pb4exZ008kq125Ap06mcRpMhURRCQH6drVbKKjCicicje5+f41TXuLnThxgkGDBqV4DEjxuM1m0+58ki2EhZkXAvffb7br9fCwOiIRyUhhYRnbTySjxMWZOjNVq8K4cVZHIyKZzcsLnnjCzIp87TWroxGR7Cw337+mKSl14cIFli9fnuyx+KV7yR232+1KSkm2cPgwtGtnCiFPm4aKyIrkQGXKZGw/kYwyebKpdbZ9O9hsVkcjIlmhf3/o1cvUL9V9p4ikJDffvzqclGrSpAk2i+6g9u7dy6ZNm9izZw+7d+8mLCwMDw8PLl26lOrjli9fzsKFCzly5Aj58uXjoYceYsyYMTRs2DBJ3+joaNasWZNwjYMHD3Ljxg1ef/11Ro4cmeaYP/rooxRnjQF069aNDz/8MM3nFed99x106WJuCoYOtToaEcksTZuaXUpCQ5Nflw/mhcEPP0CDBuDpmbXxSe60di28844Zd9qJSyT3ePhhyJsXtmyBVq2sjkZEsqv4+9eUlvDZbOZ406ZZG1dWcDgptXHjxsyMI1XTpk1j/fr1aXrMmDFjmDdvHp6enrRu3Zrr168TEhLCpk2bWLZsGR07dkzU/8SJEzz77LMZGTYANWvWpFatWknaH3rooQy/lqRs5Up49llYvNgkpkQk53J3N9vmdutm/oDfnpiKf29l6lSz+9ncufDqq+ad7FT28hBJl8OHoV8/swtXKhsZi0gOZLOZ//9LligpJSIpc3c3JWYWLkz5/nX2bNMvp3GJW/D69etTs2ZNHnjgAerVq0fFihVT7b9lyxbmzZtHsWLFCAkJoXLlygDs2rWLgIAABg0aRNOmTSlSpEjCYwoVKkTfvn158MEHqVevHmvWrGH69Onpjv3xxx9nwoQJ6T6POG/2bJgyBb78Eho3tjoaEckKgYEmGT1sWOJ3nMqXN88JgYEwejSEhJjaPjNnwn/+YxJZWl4hGSkiwhQ2f+01aNnS6mhExAp9+kC1ahAVBd7eVkcjItnR8uUQFASzZsFbb6V8/5oTuURSasSIEWnqP3fuXABGjx6dkJACaNCgAQMGDGDBggUsXbqUYcOGJRyrWLEi8+fPT/j6yy+/TGfUYrW4OBg5ElavNrsUaNttkdwlMNAkA7ZujePQob+oUaMYzZu7JXqHqVUr2LnTPE9MmABvvmmS2G3bquaPpF9sLPToYZbvaNm4SO7l62veGP3sM/jXv6yORkSymzVr4MUX4auv4MEHzT1DavevOU2Oez/4+vXrbNmyBYAuyazTim8LDg7OyrAki12/Dk89Bf/7H3z/vRJSIrmVuzu0aAEdO0bTokXyU55tNrOs9+BBcxPw3HP/JKtE0mP8eAgPhwULlOQUye3il/CJiNzu22/N88OqVSYhBY7dv+YkOS4pdfToUWJiYihevDjlypVLcrxOnToAHDp0KEvi2bt3L+PHj2fo0KFMmjSJbdu2Zcl1c7NLl6BNG4iONkUlS5WyOiIRcQXu7uam4Ngx6NwZOnY0n3/+2eLAxCV98gl89JGZip8/v9XRiIjV4v+eHDtmdSQikl3s2GF2hf/kE2jWzOporJPjklLn/l58mVxCCsDLywsfHx/Cw8O5fPlypscTHBzMnDlzWLx4MVOnTiUgIICAgAD++OOPTL92bnT6tFkmUbWqmQZZsKDVEYmIq/HwMLWofv0V6tUzzyl9+5rnFxFH7N0LQ4aYhFTZslZHIyLZgaenmcWv2VIiArBvn3kD9L//hYAAq6OxlkvUlEqLK1euAFCgQIEU+xQoUICIiAiuXLlCoUKFMiWO0qVLM2HCBNq3b8+9997LtWvX2LNnDxMnTmTbtm107dqVrVu34p7KXLyYmBhiYmISvo5PosXFxREXF5cpcbuyvXvh8cdtDB5sZ/x4s1RCP6bMET8GNQ7FFTg7Xr28YOJEGDgQpk2zUauWmUk1frydkiUzJ1ZxfX/+CZ0725gxw079+mn/O6TnV3ElGq9p06cPPPGEjddft+f45TjZkcarZBdHj0JAgI3p0+0EBiZ/r5ATxqujsee4pJT9770TbakUb7Dfvr9iJmnTpg1t2rRJ+Nrb25vHHnuMZs2a0aRJE/bu3cvKlSvp3r17iueYOXMmU6ZMSdJ+9uzZTEumuaqtW/MzbFgJJk68RLduVzh71uqIcja73U54eDg2my3V/2si2UFGjNdhw6BbN3fmzvXB39+Lfv2i+Ne/IvH2zvy/J+I6bt6EXr1K06LFDR555BJnzqT9HHp+FVei8Zo2pUuDp2dZPv74Es2bX7c6nFxH41Wyg3Pn8vDkk6UZNCiSVq0up3ivkBPGq6Mr03JcUio+WRMdHZ1in2vXrgFQ0IK1XQULFmTw4MG8/PLLfPvtt6kmpUaOHMnQ27bruXz5Mv7+/vj6+uKt/WQTfPABjBplY8UKO23bFgWKWh1SjhcXF4fdbsfX1xc3txy3ClhymIwar35+0KgRHD4Mr7xSmFatCjN2rJ3Bg82yDJGhQ20UKAD//W8+8uZ17h5Dz6/iSjRe0+7ZZ2HDhlL07q03NbKaxqtYLSwM+vWzMWSInfHjiwBFUuybE8ZrVFSUQ/1yXFKqfPnyAISGhiZ7PDo6moiICHx8fCybbVSpUiUAfv/991T7eXh44OHhkaTdzc3NZQdmRrLb4T//gXffhZAQqFPHNTPIrip+HGosiivIyPFavTp88QXs3g3jx9uYPRtee80s7cuT4/6qiqPeew++/NKMCw+P9P090vOruBKN17Tp3RveeAMiI20USfn1qGQSjVexyl9/waOPQteuMGGCzaFdeV19vDoat2t+d6nw9/fHw8ODixcvJpuY2rdvHwA1atTI4sj+ERERAVgzUyunuHnTvNO0YgV8/z38vamiiEiWeegh+OYb+PBDU6SyenX4/HPVssuNduyAUaNg9WooUcLqaEQkOytTxmz1vmKF1ZGISFaJioJ27aBpU5g2DYcSUrmJQ+/pnv27QE+ePHkoU6ZMora08PX1TfNj0srT05PmzZvz9ddfs2rVKl544YVEx1etWgVAgIUl7tesWQNA3bp1LYvBlV25YrbOjI6G7dvRu0wiYqnWrWHXLpOQmDAB3nwTpkyBNm1005EbhIaadz3ffVdvkIiIY/r3N38rBg+2OhIRyWxXr8Ljj4O/P/zf/+neMDkOJaWqVasGwP3338+PP/6YqM1RNpvN4TWF6TV06FC+/vprpk+fTkBAAJUrVwZg165dLF68GG9vb/r27Zsh14pPLH355ZeUvW3f5/nz59OnT59Es6Fu3rzJjBkzCAoKwtPTk169emVIDLnJ779D+/ZQubLZajt/fqsjEhExNxhdupibjo8+MjM5K1WCqVOhQQOro5PMcv26+b336weplIgUEUmkQwezs+vPP5tZtiKSM924Ad26mUkUH3wALroKL9M5lJSK363u9i390rqDXXp2vNu4cSPTpk1L1Hbjxg1atGiR8PXYsWMTZj+1atWKIUOGMH/+fBo1akSrVq24ceMGISEhxMXFsXjxYooWTVoM+6mnnkqo8xS/9G/RokWsX78egNKlS7Pijrm2x44dA0zC6XajRo3ilVdeoUqVKvj5+XH9+nUOHDhAWFgY+fPn5/3330+UxJK7O3LETHvs2hWmT9d/ahHJfvLkMe+AP/20mTnz+OPw8MMwaZJeeOQ0djsMGgTFi5vfr4iIozw8zN+JJUtgxgyroxGRzBAbC716mcRUUBDkzWt1RNmXQ0mpK1euONSWWS5cuMDu3bsTtdnt9kRtFy5cSHR8xowZ1KpVi4ULFxISEkLevHlp0aIFY8aMoXHjxsleZ//+/Zy5Y0/Gc+fOce7cOQD8/PwcjnncuHHs2rWLY8eOceTIEex2O+XKlWPAgAG88MIL+Pv7O3wuge++M+9GT5xotmYXEcnOPDzMc9Uzz8Dbb5vEVOfOpiB6hQoWBycZYu5cU9Nw1y5wd7c6GhFxNf37mzdbp0zRi1WRnCYuzsyaP3cOvv5aq3vuxhYdHa39SF1EVFQUZcqUITIyEm9vb6vDyTJffAH/+pfZ2ahrV6ujETCzJs+cOYOfn5/L7gYhuUd2GK8XLpilfO+9Z16ITJgAJUtaEopkgE2bTG3D7duhSpWMPXd2GK8ijtJ4dZ7dburQTZpkZtVK5tN4laxgt8NLL8HWrbB5M/j4OHeenDBeo6KiKFy4MGFhYanmL1zzu5NcY84cszxi/XolpETEdZUoYWZMHTpkNmvw94dXXzW7sYhrOXkSnnrK7LqY0QkpEck9bDbzJsWSJVZHIiIZ6bXXYONG+Oor5xNSuY2SUpItxcXBiBEwezZs22aWvoiIuDo/P3j/fbPs6+efTTH0t94yBbMl+7tyxSzDHD7cFCoWEUmPnj3Ni9eLF62OREQywsyZ5k2rb7/VjPi0cKimVGpiYmIIDg5m7969XLp0KUnB73g2m40FCxak93KSC1y/Dn37wokT5oVb6dJWRyQikrGqVoWVK2H3bhg/3iTgX3vNPPflSfdfZskMdruZ1XD//eZ3JiKSXiVKQNu2sHy5aqaKuLr//tfMiv/f/6B8eaujcS3puvX99ttvefbZZ7l4W3o/fpc9m82WqE1JKXHEpUvmXWgvL7MOt2BBqyMSEck8Dz0E33xjahSNG2d2YZo0ySxXvu3PqGQDU6bA0aOwY4d+NyKScfr3N8u5lZQScV2ffGI25AoJgcqVrY7G9Ti9fO/IkSM89dRTXLp0iSeeeIJy5coBMHbsWJ577jlq1qyJ3W4nf/78DBs2jHHjxmVY0JIz/fYbNGliaq2sXauElIjkHq1bm13cpkyBf/8b6tc3ySq7tiLJFtavN7PZ1qzR3yYRyVjt2sH587Bvn9WRiIgz1q6F55+HL7+EGjWsjsY1OZ2UmjNnDjExMUyfPp3Fixfj6+sLwMSJE3n77bfZsWMHa9aswdPTk61btzJixIgMC1pynr17oVEjUzx20SJtjSsiuY/NBoGBcPAgDBkCAwbAI4/ADz9YHVnudviwWVa5YgXce6/V0YhITpM3L/TqBR98YHUkIpJWmzaZe4RVq8zsd3GO00mpbdu24eXlRf/+/VPs07p1axYvXsy+fft4++23nb2U5HBff21mCUyeDK+8omURIpK75cljlnMcO2a2CW/f3iSrfvnF6shyn4gI6NTJ/G1q3drqaEQkp+rf39SVunHD6khExFE7d8ITT8DHH0Pz5lZH49qcTkqFhYXh6+tLvnz5AHB3dwfgxh3Ppo888gi+vr4EBQWlI0zJCWJjYcsWs+Z2yxbz9ZIlZnbUJ5+YP8giImLkz292efv1V6hdGxo3Ns+Tv/2WtG9yz6+SPrGx8PTT5uf+4otWRyMiOVmNGmYm5vr1VkciIo7Yv9/swrtwoVmCK+njdFIqf/785M+fP+Hrgn8XWfj999+T9C1evDhnzpxx9lKSAwQFQYUK0LKluclv2RKKFoWXXjJbZj76qNURiohkT97epgju8ePg4wO1aplk1Z9/muPJPb9WqGDaxXkTJ8Jff8G772oGr4hkvn79tIRPxBUcO2Zeu06fbmZKSfo5nZQqU6YMf/zxR8LX9/5daGHnzp2J+l27do1ff/3V2ctIDhAUBN26wblzidujoiAyEk6ftiQsERGXUqIEzJplak5FRZlNIZ58Mvnn19BQ067ElHM+/RQ+/ND8/G57/01EJNP06AGbN0My7++LSDbx22+m3ue4cfDMM1ZHk3M4nZSqWbMmf/75J9HR0YCpH2W323nttdf45e/CF9euXeOll14iKiqKOnXqZEjA4lpiY80Wt6ntIDV8uJaaiIg4ys8PFi+G774zSz2Se36Nb9Pza9rt2weDBsHKlfD3xsIiIpmuaFFTQ3DZMqsjEZHk/P67SUg995x5fSsZx+mk1KOPPsqtW7f49ttvE75+4IEHOHPmDA0aNMDPz4+yZcuyfPlybDYbL730UoYFLa5j27ak7+Dfzm6Hs2dNPxERcdzFi3DtWsrH9fyadhcuQOfOMHOmqSUlIpKV4pfwpfZmrohkvUuXoE0bc48wYYLV0eQ8TielHn/8cVauXEm1atUAsNlsrFy5krZt2wJw6dIlbt68SdGiRVmwYAEBAQEZE7G4lLCwjO0nIiKGnl8z1s2bpjZEhw4wYIDV0YhIbtS2rdn188cfrY5EROJdvmyKmTdubOpIqc5kxsvj7AO9vLySJJpKlChBUFAQf/75J6dPn8bT05Nq1aol7MwnuU+ZMhnbT0REDD2/ZqyXXzafZ82yNg4Ryb3c3aFPHzNb6qGHrI5GRK5dg44doXJlmD9fCanM4nRSKjUlS5akZMmSmXFqcTFNm0L58qbobnJTkW02c7xp06yPTUTEld3t+RXMC5w33oACBaB+/ayNz5UsXgxr15rZCXnzWh2NiORm/fpBw4bw9tvaaEHESjdumBnU3t6wZIm5p5LM4fTyPRFHuLvDnDnm33dmluO/nj1b/8lFRNLqbs+vNpspmPv446Z4bteucPhw1seZ3e3cCSNGwOrVZodDEREr3X8/VK1qnpNExBqxsWbW4rVrZkdevWGVuZSUkkwXGJj8Lkbly5v2wEBr4hIRcXV3e3596il46SX49VeoWRMaNTJbGJ85Y0282c358+ZnOH8+1K1rdTQiIkb//mZmhohkPbsdBg6E336DNWs0YzErOLx8r1ChQum6kM1mIyoqKl3nENcVGAidOpldoMLCTI2Tpk01Q0pEJL0ceX719obXXoPnn4cpU6BGDVPMe/z43Ds76Pp187Pr0wd69LA6GhGRfzz5pKlzd+6ceZNBRLKG3W7+7/34I2zeDAULWh1R7uDwTCm73Z7uD8nd3N2hRQtz89+ihRJSIiIZxdHn1xIlTCHvgwchMhLuu88kq3Lbe0Z2OwweDEWLwuTJVkcjIpJY4cLmzYalS62ORCR3ef112LABvvoKihSxOprcw+GZUu+++26y7Xa7ncGDB3PfffcxYsSIDAtMREREMsc995ji3iNHwsSJUKmSmTU1eHDumKb+zjuwfTv88IPeIBGR7Kl/f/OcPG6cdvwSyQpvv22WzW7bBqVKWR1N7uJwUqpXr14pHhs8eDAlSpRItY+IiIhkL9WqQVAQ7NplklKzZpmZU336QJ5M2Z/XeiEh5nvcvh18fKyORkQkeS1bQkwM7NgBDz9sdTQiOduiRTBzJvzvf+Dra3U0uY8KnYuIiORyDRrApk1m9tT8+aYo+hdfmGVuOcmpU6b4+5IlZncrEZHsys0N+vaFDz6wOhKRnG3FCvPG3FdfQeXKVkeTOykpJSIiIgA88gjs3g2TJsGECf8kq3KC6Gjo3BmGDoWOHa2ORkTk7vr1g88/N89fIpLx1q+HIUPgyy/NG3JiDSWlREREJIHNBl27wqFDZkvkfv3+SVa5Krvd1Ge57z6TbBMRcQUVK0LdumaZtYhkrM2boXdv8/+rfn2ro8ndlJQSERGRJPLkgQED4PhxeOwx89G1Kxw+bHVkaTdtGhw5YpbtuenOR0RcSL9+WsInktF27TL3NMuXm12LxVq6NRMREZEU5c8PL78Mv/4KNWpAo0bwzDNw5ozVkTnmyy/NjjqrV0PBglZHIyKSNt26wY8/wunTVkcikjMcOAAdOsC775o33MR6SkqJiIjIXXl7w+uvw7Fj5t81asBLL8GFC1ZHlrIjR8xOgp98YpbBiIi4moIFTWLqww+tjkTE9R07Bo8+Cm++CU8+aXU0Es/hDZ+/++67VI9HRkbetU+TJk0cvZyIiIhkQyVLwuzZJiH12mumTtNLL5nZVIUKWR3dPyIjTWHziRNNTSwREVfVr5/Zie/f/9YSZBFnnTlj7gfGjDEzviX7cDgpFRAQgM1mS/aYzWbjl19+oV27dik+3mazERUVlfYIRUREJNu55x5T52TUKJP4qVTJbKk8aJBZ8mel2Fjo2dMULh0+3NpYRETSq2lTcHeH//1P9W9EnPHHHyYh9a9/6b4gO0pTrt1ut6frQ0RERHKWatXMzjXr1sHateDvb5JVt25ZF9Mrr8Cff8LChWY3QRERV2azqeC5iLMuXYI2baBjRzPbULIfh2dK/fLLL5kZh4iIiLiwBg1g0yb49lsYNw6mT4fJk6FLl6xNDH3+OSxeDLt3g6dn1l1XRCQz9ekD1avDvHnZa6m0SHZ2+bIpZt6wIcyYoTeqsiuHk1J+fn6ZGYeIiIi4OJvNvBv5yCPwxRcmOTVtGkydCq1bZ/719++H554zO+6VL5/51xMRySp+fuaF9eefqx6OiCOuX4dOneDee2HBAiWksjOVyhMREZEMZbOZ3aJ+/hkGDjTLTh55xMxeyiwXL5qbz+nToXHjzLuOiIhV+vfXEj4RR9y8CU88YXavXLrU1GST7EtJKREREckUefLAgAFmC+Z27cwU+m7d4MiRjL3OzZtma+fHHoNnn83Yc4uIZBedO8PBg3D8uNWRiGRfsbFmuevVq/DZZ5A3r9URyd04vHzvTsuXL3e4r7u7O97e3lSoUIGqVaumuItfSvbu3cumTZvYs2cPu3fvJiwsDA8PDy5dunTXGBcuXMiRI0fIly8fDz30EGPGjKFhw4ZJ+kZHR7NmzZqEaxw8eJAbN27w+uuvM3LkyDTFe7vg4GBmz57NgQMHsNvt1K5dm+HDh6e6U6GIiEhO4ukJI0aYXW/eesssQenaFV591SxJSa+RI01h9dmz038uEZHsqkAB6N4dPvwQJk2yOhqR7Mduh8GD4dQpU+PS6t2AxTFOJ6UGDhyY5uQSQLFixRg0aBAjR44kTx7HLj9t2jTWr1+fpuuMGTOGefPm4enpSevWrbl+/TohISFs2rSJZcuW0bFjx0T9T5w4wbMZ/Pbq/PnzGTVqFHny5KFly5Z4eHiwadMmunXrxowZMxgyZEiGXk9ERCQ7K1wY3ngDXnjBFEGvUcMkqsaNgxIlnDvnBx/AqlXw44+QL1/Gxisikt3072+WJb3+upYkidzObjdvUu3aBZs3m6V74hqcXr7XpEkTmjRpQt68ebHb7eTLl4/q1avTsGFDqlevjoeHR0L7ww8/TJ06dShUqBAXL15k8uTJdO3albi4OIeuVb9+fcaNG8fKlSs5efLkXftv2bKFefPmUaxYMXbu3Mmnn37KmjVr+Prrr3F3d2fQoEGEh4cnekyhQoXo27cv77zzDtu3b2f06NFO/VziHT9+nHHjxuHh4cHXX3/N6tWr+fTTT/n+++8pVqwY48aN48SJE+m6hoiIiCsqWRLmzIEDB8xWzf7+5gXW5ctpO8+uXfDyy7B6tTmniEhO16ABeHmZ3U5F5B//+Q+sWwdffw1Fi1odjaSF00mp4OBgSpYsSd68eZkzZw6hoaHs3LmTb775hp07d3Lu3Dnmzp1L3rx5KVWqFNu2bePcuXO89957FCpUiJCQEJYtW+bQtUaMGMHEiRNp164dpUqVumv/uXPnAjB69GgqV66c0N6gQQMGDBhAZGQkS5cuTfSYihUrMn/+fJ555hnq1Knj8CyulMyfP59bt24xYMAAGjRokNB+3333MWrUKG7dusWCBQvSdQ0RERFXVqECLFkC330H+/ZBpUpmCd7160n7xsbCli3wySfm89mzEBgI8+dDvXpZGbWIiHVsNjNbaskSqyMRscad9wOxsebe4f33zZI9B9IFks04nZT673//S1BQEEuXLmXAgAHkv2PBZv78+XnmmWf48MMPCQoKYsGCBbi5ufHUU08xb9487HY7K1asSPc3cKfr16+zZcsWALp06ZLkeHxbcHBwhl/7dvHnTy6GwMBAADZs2JCpMYiIiLiC6tXNEry1a2HNGrj/frMs79YtczwoyCSwWraEp582nytXhocegh49LA1dRCTL9e5tni8jIqyORCRrJXc/UKKEmW397bcZU6dSsp7TSakPP/wQX19fHn300VT7Pfroo/j5+SWamRQYGEjRokX5+eefnb18io4ePUpMTAzFixenXLlySY7XqVMHgEOHDmX4teNFRERw9uxZAGrXrp3keLly5ShevDhnzpwhMjIy0+IQERFxJQ0bQkgILFoE8+ZBrVowerTZse/cucR9b9wwL8qCgqyJVUTEKmXLQrNmkAnv74tkW0FByd8PhIdDZKTZmVJck9NJqRMnTlDCwaqkxYsXT1IL6t577yUqKsrZy6fo3N+jNLmEFICXlxc+Pj6Eh4dzOa3FKxwUn5AqUqQIXl5eyfYpW7Zsor4iIiJilqa0bQu7d5vd+WbNMsVLUzJ8uJm6LyKSm/Tvb2aUiuQGsbEwbJjuB3Iqpwsn5cuXjxMnTnDjxg3ypbLdzY0bNzhx4gR58+ZN1B4VFUWhQoWcvXyKrly5AkCBAgVS7FOgQAEiIiK4cuVKpsQQHR0NgKenZ4p94pNV8X2TExMTQ0xMTMLX8Um0uLg4h4vEi2SG+DGocSiuQOPVdZUoAbdupfz+md1uaktt3RpHixZZF1dm0ngVV6Lxap0OHWDQIBuHDtmpVs3qaFyDxqvr2roVzp3T/YCrcTR2p5NSDzzwACEhIUycOJHp06en2O/f//43kZGRtG7dOqEtNjaW3377jYoVKzp7+RTZ/06f2my2u/bJLBkVw8yZM5kyZUqS9rNnz2ZKMk3EUXa7nfDwcGw2W6rjXCQ70Hh1XYcOeQF3n5V96NBfVKyY8ps8rkTjVVyJxqu1OnQoyjvv2Bk3LvzunUXj1YXpfsA1x6ujK9OcTkqNGDGCkJAQFixYwIEDB+jXrx+1atXC29ubqKgoDh48yJIlS/juu+9wc3Nj5MiRCY/95ptvuHHjBo0bN3b28imKT9akNgPp2rVrABQsWDDDr3/7ea9evZpin/hjKS3vAxg5ciRDhw5N+Pry5cv4+/vj6+uLt7d3BkUrknZxcXHY7XZ8fX1xc3N6FbBIltB4dV01ajjarxh+fsUyN5gsovEqrkTj1VovvAAdO9p4551CpHPj8FxB49V16X7ANcero+WanH76atasGXPmzGHEiBF89913bN++PUkfu91O3rx5eeutt2jatGlC+5UrV3j55ZeT3ZkuvcqXLw9AaGhossejo6OJiIjAx8cn02Yb+fr6AhAeHk50dHSyiafz588n6pscDw8PPDw8krS7ubm57MCUnCN+HGosiivQeHVNzZtD+fIQGpp8HQmbzRxv3tyNnPSr1XgVV6Lxap0HHzTLnL/5xkb79lZH4xo0Xl2T7gdc85tyNO50fXfPPPMMO3bsoGfPnpQsWRK73Z7wUaJECXr16sWOHTt45plnEj2uW7duvPHGG9StWzc9l0+Wv78/Hh4eXLx4MdnE1L59+wCo4Wi61Qk+Pj4Jyab9+/cnOR4aGsrFixfx9fWlcOHCmRaHiIiIK3N3hzlzzL/vnLke//Xs2aafiEhuY7Op4LnkDvH3AyklpED3A64s3Sm3qlWrsnDhQn799VfOnz/PsWPHOH/+PCdPnuTdd9+latWqGRGnwzw9PWnevDkAq1atSnI8vi0gICBT44g/f3IxBP29f3W7du0yNQYRERFXFxgIK1fCnZvqli9v2gMDrYlLRCQ76NkTgoPh4kWrIxHJXIGBUKcO+Pgkbtf9gOvL0Hlg3t7elC1b1vJ6R/F1mKZPn86JEycS2nft2sXixYvx9vamb9++GXKtunXrUrdu3YTlePGGDBmCu7s777//Pj/88ENC+4kTJ5gxYwbu7u4MHjw4Q2IQERHJyQID4fRp2LwZPv7YfD51SjegIiIlS0KbNvDJJ1ZHIpK5fvoJzp2D337T/UBO4xIl8TZu3Mi0adMStd24cYMWt+33OHbs2ITZSa1atWLIkCHMnz+fRo0a0apVK27cuEFISAhxcXEsXryYokWLJrnOU089xe+//w78U5Nq0aJFrF+/HoDSpUuzYsWKRI85duwYADdv3kzU7u/vz+TJkxk7dixt2rShVatW5MuXj02bNnHt2jWmTZuGv79/On4qIiIiuYe7Ozlmm2cRkYzUvz+8/jrctj+SSI4zbZoZ497euh/IadKdlDpz5gzBwcGcOHGC6Oho4uLiku1ns9lYsGCBU9e4cOECu3fvTtRmt9sTtV24cCHR8RkzZlCrVi0WLlxISEgIefPmpUWLFowZMybFXf/279/PmTNnErWdO3eOc+fOAeDn55emuIcOHUqlSpWYPXs2O3bsAMzMquHDh9Ne1QhFRERERCSdHnsMnn0W9u+H2rWtjkYk4x07Bl99Be++a3Ukkhls0dHRyZQLc8yrr77K7NmzExJR9uQqj8VfyGbj8uXLzl5KMFsqlilThsjISMuXSEruFhcXx5kzZ/Dz83PZ3SAk99B4FVei8SquROM1+3j5ZVMEetYsqyPJvjReXde//mVqSc2caXUkWScnjNeoqCgKFy5MWFhYqvkLp2dKvf/++7z11lsAVK9enQYNGlCyZEmX/YGJiIiIiIi4on79oHVrePNNyJfP6mhEMs65c/Dpp3D0qNWRSGZJV1LKZrPx6quvMnLkyIyMSURERERERBxUqxb4+cGXX0KXLlZHI5JxZs2CHj2gbFmrI5HM4vS0puPHj1O0aFElpERERERERCzWvz8sWWJ1FCIZ56+/4L33YPRoqyORzOR0UsrDw4MKFSpkYCgiIiIiIiLijB49YNMm+OMPqyMRyRjz5kFAAFSubHUkkpmcTkrVqlUryU51IiIiIiIikvWKFYN27WDZMqsjEUm/K1fgnXdg7FirI5HM5nRSaujQoVy4cIEVK1ZkZDwiIiIiIiLihPglfKlsii7iEhYtgocegrp1rY5EMpvTSal27drxyiuvMHToUN58800iIiIyMCwRERERERFJi7ZtTR2ePXusjkTEeTEx8NZbMG6c1ZFIVnB6973q1asDcOvWLSZNmsSkSZMoVqwYXl5eyfa32WwcOnTI2cuJiIiIiIhIKvLkgd694YMP4MEHrY5GxDnLl8M990DTplZHIlnB6aTUb7/9lqTt4sWLXLx4Mdn+NpvN2UuJiIiIiIiIA/r3h8aNzUyT/PmtjkYkbWJj4c03zfhVCiF3cDopFRwcnJFxiIiIiIiISDpVqQL33w9r18KTT1odjUjarFoF+fLBY49ZHYlkFaeTUk01l05ERERERCTb6dfPLOFTUkpcid0OU6eaHffcnK5+La5Gv2oREREREZEc5KmnYNs2CA21OhIRx33zDYSHQ/fuVkciWUlJKRERERERkRykcGHo1Ak++sjqSEQcN3UqjBplCvZL7uHQr3v58uUAFC5cmA4dOiRqS4uePXum+TEiIiIiIiKSNv36wQsvwJgxKhgt2d/OnXD4sCnUL7mLQ0mpgQMHYrPZ8Pf3T0hKxbelhZJSIiIiIiIima9VK7h2Db7/3uzGJ5KdTZsGL72kHSNzI4eSUk2aNMFms1G+fPkkbSIiIiIiIpK9uLtD376wZImSUpK9/fwzbN0KS5daHYlYwaGk1MaNGx1qExERERERkeyhb1944AGYPRsKFLA6GpHkvfkmDB4M3t5WRyJWUKFzERERERGRHKhyZahdG4KCrI5EJHmnT8Pq1TB8uMWBiGWUlBIREREREcmh+vc3S/hEsqOZM6FPHyhZ0upIxCrp2mxx3759bN++ndDQUC5fvkyBAgWoWLEijRs3pmbNmhkVo4iIiIiIiDihWzd48UX47Te45x6roxH5xx9/mDpSBw5YHYlYyamk1Lp163jjjTc4cuRIin1q1qzJf/7zH1q3bu10cCIiIiIiIuK8QoWga1f48EN45RWroxH5x9y50LkzVKhgdSRipTQv3xs7dixPP/00hw8fxm63Y7fb8fb2pkyZMnh7eye0HThwgM6dOzNt2rREjz937hwrVqzIsG9AREREREREUha/hC8uzupIRIyoKJg/H8aMsToSsVqaklIzZsxg3rx52O12GjRowIcffsjp06cJDQ3l2LFjhIaGcvr0aT788EMaNmyI3W5n8uTJfPjhhwCcPHmSNm3acOrUqUz5ZkRERERERCSxpk3BZoNt26yORMRYsACaNYPq1a2ORKzmcFLqzJkzTJ06FZvNxhtvvMGmTZvo2rUrxYsXT9SvePHidO3alW+//ZbXXnsNu93O+PHj2blzJ23btuXcuXO4u7tn+DciIiIiIiIiSbm5Qd++8MEHVkciAteuwaxZMG6c1ZFIduBwUuq9997jxo0bPPfcc7z88ssOPWbkyJE8++yzREZG0rZtW37//Xfq1KnDwIEDnQ5YRERERERE0qZvXwgKgitXrI5EcrslS6BqVWjY0OpIJDtwOCkVEhKCm5sbo0ePTtMFxowZg81mIy4ujtq1a7N27VoKFy6c5kBFRERERETEOffcA/Xrw+efWx2J5Ga3bsGMGZolJf9I0/K9ypUrU6pUqTRdoHTp0lSuXBmbzcb69espUqRImoMUERERERGR9OnXT0v4xFqffgpFikCbNlZHItmFw0mpy5cv4+3t7dRFvL29yZMnjxJSIiIiIiIiFgkMhAMH4MQJqyOR3Mhuh2nTzCwpm83qaCS7cDgpVbRoUcLCwpy6SFhYmBJSIiIiIiIiFipQAJ58Ev7eHF0kS335Jdy4AV26WB2JZCcOJ6WqVKnC+fPnOXDgQJousH//fs6fP0+VKlXSHJyIiIiIiIhknH79TFIqNtbqSCQ3sdth6lQYPRrc3a2ORrITh5NSjz76KHa7nbFjxxLr4DNYbGwsY8eOxWazERAQ4HSQIiIiIiIikn6NGpkZU5s3Wx2J5CbbtsFvv0Hv3lZHItmNw0mpZ555hmLFirFt2zaefvpp/vrrr1T7X7p0iR49erBt2zaKFi3KM888k+5gRURERERExHk2mwqeS9abOhVGjIB8+ayORLKbPI52LFiwIO+++y7du3dnw4YN1KpVi+7du9OiRQsqVqxIwYIFuXLlCidPnmTLli189tlnREZG4u7uzoIFCyhYsGBmfh8iIiIiIiLigN694f77ISICfHysjkZyur17Yfdu+PxzqyOR7MjhpBRAu3bt+OCDD3j++eeJjIxk0aJFLFq0KNm+drsdLy8v5s2bx2OPPZYhwYqIiIiIiEj6lCsHTZvCZ5/Bc89ZHY3kdG++CUOHguapSHIcXr4Xr2vXrvzwww8888wzeHt7Y7fbk3wUKlSIZ555hl27dvHEE0+kO8i9e/cyc+ZMevToQeXKlfHy8qJo0aJ3fdzy5ctp1qwZJUuWpHz58nTp0oWdO3em+pidO3fSpUsXypcvT8mSJWnWrBnLli1Lc8wfffQRXl5eKX707ds3zecUERERERHJCP37awmfZL4TJyA4GF54wepIJLtK00ypeH5+fsydO5fZs2dz8OBBQkNDuXz5MoUKFaJcuXLUrFkTN7c057tSNG3aNNavX5+mx4wZM4Z58+bh6elJ69atuX79OiEhIWzatIlly5bRsWPHJI9Zu3YtvXr1Ii4ujiZNmlCsWDG2bNnCwIEDOXjwIG+++WaaY69Zsya1atVK0v7QQw+l+VwiIiIiIiIZoWNHGDQIDh+GqlWtjkZyqunTYcAAKFbM6kgku3IqKRXPzc2N2rVrU7t27YyKJ1n169enZs2aPPDAA9SrV4+KFSum2n/Lli3MmzePYsWKERISQuXKlQHYtWsXAQEBDBo0iKZNm1KkSJGEx4SHhzNo0CBiY2P5+OOP6dSpEwB//PEHbdq0SViG2Lx58zTF/vjjjzNhwoQ0fsciIiIiIiKZJ39+6NEDliwxy6tEMtr58/DJJybxKZKSjJvOlIlGjBjBxIkTadeuHaVKlbpr/7lz5wIwevTohIQUQIMGDRgwYACRkZEsXbo00WOWLFlCZGQkHTp0SEhIAZQqVYpJkyYB8M4772TEtyMiIiIiImK5/v1h6VLYtMkkD7ZsgdhYq6OSnOLtt6F7dyhf3upIJDtziaRUWly/fp0tW7YA0KVLlyTH49uCg4MTtW/cuBGAzp07J3lMQEAA+fPnZ/PmzVy/fj1jAxYREREREbHAb7/BxYvwyCPw9NPQsiVUqABBQVZHJq7u0iVYtAhGj7Y6EsnuclxS6ujRo8TExFC8eHHKlSuX5HidOnUAOHToUKL2+K/jj98uX758VKtWjevXr3Ps2LE0xbN3717Gjx/P0KFDmTRpEtu2bUvT40VERERERDJaUBA88QTcupW4PTQUunVTYkrSZ948ePRR8Pe3OhLJ7nJcUurcuXMAySakALy8vPDx8SE8PJzLly8DEBUVRURERKqPi2+PP7+jgoODmTNnDosXL2bq1KkEBAQQEBDAH3/8kabziIiIiIiIZITYWBg2DOz2pMfi24YP11I+cU50NMydC2PHWh2JuIJ0FTrPjq5cuQJAgQIFUuxToEABIiIiuHLlCoUKFSI6OjrRsZQec/v576Z06dJMmDCB9u3bc++993Lt2jX27NnDxIkT2bZtG127dmXr1q24u7uneI6YmBhiYmISvo5PosXFxREXF+dQHCKZIX4MahyKK9B4FVei8SquROPVdW3dCufOpTw/wW6Hs2dh69Y4WrTIurgyk8Zr1lm0COrVs1Gnjh39uJ2TE8aro7HnuKSU/e/Uvs1mu2uflL525DF306ZNG9q0aZPwtbe3N4899hjNmjWjSZMm7N27l5UrV9K9e/cUzzFz5kymTJmSpP3s2bMUKlQoTfGIZCS73U54eDg2my3V/2si2YHGq7gSjVdxJRqvruvQIS+ghAP9/qJixei79nMFGq9Z48YNmD69PG+/fZEzZ1SP2Vk5YbzGT6q5G4eSUmfPnsXDw4OSJUumK6isEJ+suX32052uXbsGQMGCBRN9Brh69Sre3t53fYyzChYsyODBg3n55Zf59ttvU01KjRw5kqFDhyZ8ffnyZfz9/fH19U02RpGsEhcXh91ux9fXFze3HLcKWHIYjVdxJRqv4ko0Xl1XjRqO9iuGn1+xzA0mi2i8Zo0PPoB77rHxxBMlcdFcSraQE8ZrVFSUQ/0cSkpVrVqVhx9+mK+++iqhberUqZQvX57evXs7F2EmKf/3fpOhoaHJHo+OjiYiIgIfH5+EBJa3tzeFCxcmMjKS0NDQZBM+8ecrnwH7WVaqVAmA33//PdV+Hh4eeHh4JGl3c3Nz2YEpOUf8ONRYFFeg8SquRONVXInGq2tq3hzKlzdFzVNaEJI/P9x/vxs56Ver8Zq5YmNhxgyYPh3c3ZWRSi9XH6+Oxu3wd3fn8rXJkyfz0UcfpS2qLODv74+HhwcXL15MNjG1b98+AGrc8fZAzZo1Ex2/3c2bN/nll1/w8PDAPwO2D4gvqp7eWVciIiIiIiJp5e4Oc+aYf985m8VmMx+tWkG9erBuXdbHJ65p9WrIkwc6dLA6EnElDiWlPD09CQ8Pz+xYMoSnpyfNmzcHYNWqVUmOx7cFBAQkan/00UcBWL16dZLHBAcHc/36dVq0aEH+/PnTHeOaNWsAqFu3brrPJSIiIiIiklaBgbByJdy5+Xj58qb9yy9h6VIYOBAGDTI7qomkxG6HadPMjnsuOrFHLOLQcKlcuTJHjx7liy++4Pr17F+sLL4O0/Tp0zlx4kRC+65du1i8eDHe3t707ds30WP69euHt7c369evT0gaAfz5559MnDgx0XlvV7duXerWrcv58+cTtc+fPz/JTn03b95kypQpBAUF4enpSa9evdL3jYqIiIiIiDgpMBBOn4bNm+Hjj83nU6dMO8Cjj8KBA/DHH2bW1I8/WhquZGObNsGFC5BKyWSRZDlUU6pv376MHDmSfv36JWr//vvvHd4FzmazOVzo6k4bN25k2rRpidpu3LhBi9v2Jx07dmzC7KdWrVoxZMgQ5s+fT6NGjWjVqhU3btwgJCSEuLg4Fi9eTNGiRROdr2jRoixYsIDevXvTs2dPmjZtSrFixdi8eTMREREMHjyYli1bJont2LFjgEk43W7UqFG88sorVKlSBT8/P65fv86BAwcICwsjf/78vP/++5QtW9apn4eIiIiIiEhGcHeH215WJVG8OAQFwfvvQ5s2MGoUjBljHicSb+pUMzby5rU6EnE1DiWlBg0aRFRUFPPnz+fixYsJ7XfWmUpNWvre6cKFC+zevTvJ+W5vu3DhQqLjM2bMoFatWixcuJCQkBDy5s1LixYtGDNmDI0bN072Op07d+brr7/mzTffZPfu3dy4cYP777+f5557jj59+qQp5nHjxrFr1y6OHTvGkSNHsNvtlCtXjgEDBvDCCy9kSG0qERERERGRzGazwb/+ZQqk9+oFwcHw0UdQoYLVkUl28MMPcOgQrF9vdSTiimzR0dFpyhZduHCBa9euUa1aNR544IE0FTv38/NLc4Dyj6ioKMqUKUNkZGSyOwSKZJW4uDjOnDmDn5+fy+4GIbmHxqu4Eo1XcSUar7nTzZvwn//AvHkwdy707Jm0WHp2pPGaebp0gfr1Ydw4qyPJOXLCeI2KiqJw4cKEhYWlmr9waKbU7UqUKJHwbw8PDyWaREREREREcom8eeGNN0y9qd69TUH0BQvAx8fqyMQKv/wCW7bAkiVWRyKuyumU2y+//JKmWVIiIiIiIiKSMzz8MOzbB/nyQa1aJjEhuc/06WZ3xsKFrY5EXFWaZ0rFS26GVExMDFFRUXh7e+Ph4ZGuwERERERERCT78vaGDz+Ezz6Dbt1gwACztC9fPqsjk6xw5owpgv/33l8iTkn34sQzZ87w8ssvU61aNYoXL07FihUpXrw41atXZ+TIkZw5cyYj4hQREREREZFs6MknzaypH3+Ehg3h8GGrI5KsMHOmKXxfurTVkYgrS1dSauPGjTRo0IBFixZx5swZ7HZ7wsdvv/3GwoULadCgARs3bsyoeEVERERERCSbKV8evvnGFD5v1Aj+7/8gHRuwSzZ34YKpIzVqlNWRiKtzOil16tQpevfuzeXLl/H19WX69Ols2LCBn376iQ0bNjBjxgz8/Py4fPkyffr04dSpUxkZt4iIiIiIiGQjbm4wYgRs3Qrz50OHDvDHH1ZHJZlhzhzo2BHuvdfqSMTVOZ2UmjVrFteuXaNr164cOHCAIUOG0KxZM/z9/WnWrBmDBw9m//79dO3alatXrzJnzpyMjFtERERERESyodq1zVK+SpVMEfR166yOSDJSVJRJOo4da3UkkhM4nZTavHkznp6evPPOO+TJk3y99Dx58vDOO+/g6enJpk2bnA5SREREREREXIenJ8ydawqhP/ec2aEtOtrqqCQjvPsuNGkCNWpYHYnkBE4npc6fP0+VKlXw9vZOtZ+3tzdVqlTh/Pnzzl5KREREREREXFBAABw4AL//Dg88AHv2WB2RpMf16zBrlmZJScZxOinl4eFBtIOp7ujoaDw8PJy9lIiIiIiIiLioEiVg1SpTb+qRR2DqVIiNtToqccaHH4K/PzRubHUkklM4nZTy9/fn+PHjHDx4MNV++/fv59ixY/j7+zt7KREREREREXFhNhs8+yz88INJULVsCb/9ZnVUkha3bsH06TBunNWRSE7idFIqMDAQu93O008/zc6dO5Pts3PnTnr27InNZqNr165OBykiIiIiIiKu7777YPt2aN4c6tWD5cutjkgc9fnnULgwPPqo1ZFITpJ8hXIHDBw4kI8//phDhw7Rpk0bHnroIapXr06pUqX4448/+Pnnn9m9ezd2u51atWrx3HPPZWTcIiIiIiIi4oLy5oX//MckN3r3hi+/NLu5+fhYHZmkxG6HadNgwgQz600kozidlPLw8GDt2rUMGDCAzZs388MPP7B79+6E43a7HYDWrVuzaNEi1ZQSERERERGRBE2awL598OKLULs2LF1qZlBJ9rNhA1y7BloAJRnN6aQUQMmSJVm3bh27du0iODiYY8eOERUVhbe3N/7+/rRr144GDRpkVKwiIiIiIiKSgxQubIpnf/aZSXg8+yy8/jrky2d1ZHK7adNg9Ghwd7c6Eslp0pWUitegQQMln0RERERERMQpTz4JjRpB377m8/LlUKWK1VEJwHffwcmTZqmlSEZzutC5iIiIiIiISEbx9YVvv4WnnoKGDU2dqb+rwoiFpk6Fl18GVeSRzKCklIiIiIiIiGQLbm4wahRs2QLz5sHjj8Mff1gdVe61fz/s3Anat0wyi5JSIiIiIiIikq3UqQN79sC990KtWrB+vdUR5U7TpsELL0ChQlZHIjmVklIiIiIiIiKS7Xh6wjvvwJIlpgD6kCFw9arVUeUeJ06YXfdefNHqSCQnU1JKREREREREsq127eDAAQgNhXr1zAwqyXwzZsAzz0CxYlZHIjmZklIiIiIiIiKSrZUoAatXm4LbjzxilpXFxlodVc4VFgYff2x+3iKZSUkpERERERERyfZsNlNwe9cu+OILaNUKzpyxOqqcadYseOIJsyOiSGZSUkpERERERERchr8/7NgBTZtC3bpmRo9knPBw+O9/YcwYqyOR3EBJKREREREREXEpefPCpElmSd/48dCzJ0REWB1VzvB//2eWSN5/v9WRSG6Qx5FO3333HQAFChSgXr16idrSokmTJml+jIiIiIiIiEhymjaF/fvhhRegdm346CNo1szqqFzX1aswZw5s3Gh1JJJbOJSUCggIwGaz4e/vz56/tzqIb3OUzWYjKirKuShFREREREREklG4sElGrVgBXbqYulOvvw758pnjsbGwdSscOuRFjRrQvDm4u1sbc3b13ntmSeQDD1gdieQWDiWlfH19sdlslC5dOkmbiIiIiIiIiNWeegoefhj69IHGjWHZMvjlFxg2DM6dcwNKAFC+vJkNFBhobbzZzc2bMHMmLFlidSSSmziUlDp8+LBDbSIiIiIiIiJW8fWFb7+Ft94ys32uXk3aJzQUunWDlSuVmLrdxx9DmTLQsqXVkUhuokLnIiIiIiIikmO4u8OIEVCwYPLH7Xbzefhws7RPIC4O3nwTxo0DLYiSrKSklIiIiIiIiOQo27bBn3+mfNxuh7NnTT+BNWtMMqpjR6sjkdzGoeV7d/PHH3/w7bffcuTIEa5cuULBggWpUqUKrVu3TlSHSkRERERERCSzhYVlbL+czG6HqVNhzBhw07QVyWLpSkpdu3aNCRMm8MEHH3Dr1q2kJ8+Th379+jFp0iS8vLzScykRERERERERh5Qpk7H9crKQEDOrrEcPqyOR3MjpPOiNGzfo2LEjixYt4ubNm5QoUYImTZoQGBhIkyZNKFmyJDdv3uS9996jU6dO3Lhxw+kg9+7dy8yZM+nRoweVK1fGy8uLokWL3vVxy5cvp1mzZpQsWZLy5cvTpUsXdu7cmepjdu7cSZcuXShfvjwlS5akWbNmLFu2zOnYg4ODefTRRylTpgylS5fm0UcfJTg42OnziYiIiIiISOqaNjW77KVWH8lmg/feg1Onsi6u7GjqVBg5EvLmtToSyY2cTkrNmTOH77//niJFivDf//6X48ePExwczIcffkhwcDDHjx9n0aJFFC1alF27djFnzhyng5w2bRqvvvoqa9euJczB+ZVjxozhueee45dffqFly5Y88MADhISE0LZtW9auXZvsY9auXUvbtm355ptvqFGjBm3atOHXX39l4MCBjBkzJs1xz58/n27durFz504aNGhA8+bN2bNnD926dWP+/PlpPp+IiIiIiIjcnbs7xL8EvTMxZbOZj8WLoXRpqFMHhg6FP/7I8jAtt3s3HDgAzzxjdSSSWzmdlPr000+x2Wx8/PHHPP3007i7uyc+sZsbPXr0YPny5djtdj799FOng6xfvz7jxo1j5cqVnDx58q79t2zZwrx58yhWrBg7d+7k008/Zc2aNXz99de4u7szaNAgwsPDEz0mPDycQYMGERsby/Lly9m4cSPLly/np59+olKlSsybN4+tW7c6HPPx48cZN24cHh4efP3116xevZpPP/2U77//nmLFijFu3DhOnDiR5p+FiIiIiIiI3F1gIKxcCeXKJW4vX9609+sHM2fCoUNw/Tr4+8PEiRAZaUm4lpg2DYYNgwIFrI5Eciunk1KnTp2iQoUKNGnSJNV+TZo0oWLFipw+fdrZSzFixAgmTpxIu3btKFWq1F37z507F4DRo0dTuXLlhPYGDRowYMAAIiMjWbp0aaLHLFmyhMjISDp06ECnTp0S2kuVKsWkSZMAeOeddxyOef78+dy6dYsBAwbQoEGDhPb77ruPUaNGcevWLRYsWODw+URERERERCRtAgPh9GnYtCmOOXMusGlTHKdOmfZ4vr6waBHs2gVHj0KlSjBjBly7ZlnYWeLIEdi0CZ5/3upIJDdzOilVsGBBihQp4lBfHx+fLCt0fv36dbZs2QJAly5dkhyPb7uzrtPGjRsB6Ny5c5LHBAQEkD9/fjZv3sz169cdiiP+/MnFEPj3M+CGDRscOpeIiIiIiIg4x90dWrSAjh2jadHCfJ2cKlXg889h40b45hu47z6TrEpmT68c4c03YeBA8PGxOhLJzZxOSjVo0ICjR49y9erVVPtdvXqVo0eP0rBhQ2cvlSZHjx4lJiaG4sWLU+7OeZpAnTp1ADh06FCi9viv44/fLl++fFSrVo3r169z7Nixu8YQERHB2bNnAahdu3aS4+XKlaN48eKcOXOGyNw0N1RERERERCSbe/BB+PprWLrUFEKvVg0++wzi4qyOLOOcOQNffAEvvWR1JJLbOZ2UGjFiBDExMYwYMQK73Z5iv5EjRxITE8PIkSOdvVSanDt3DiDZhBSAl5cXPj4+hIeHc/nyZQCioqKIiIhI9XHx7fHnT018QqpIkSIpzhArW7Zsor4iIiIiIiKSfbRqBTt3wvTp8NprJln11VeQystfl/HWW9Czpyn0LmKlPM4+sGzZskyZMoUJEyawa9cuBg8eTLVq1ShVqhR//vknv/zyC/Pnz+f06dNMnTqV0qVLJ5uA8fX1Tdc3cKcrV64AUCCVSm0FChQgIiKCK1euUKhQIaKjoxMdS+kxt58/NfHn8/T0TLFPfLLq9mvfKSYmhpiYmISv45NocXFxxOWkNL24nPgxqHEorkDjVVyJxqu4Eo1XcSXpGa8dO0L79vDRR/DcczYqVoTJk+1k0WKgDHfxInzwgY09e+w5avZXTpITnl8djd3ppFS1atUS/n38+HFefvnlFPuOHj2a0aNHJ2m32WxERUU5G0Ky4mdt2e7c9zOZPil97chjMjqG5MycOZMpU6YkaT979iyFChVyOB6RjGa32wkPD8dms6U6zkWyA41XcSUar+JKNF7FlWTEeG3VCh5+GD7+uBAdO/pQr14MI0aEc//9NzM42sz19ts+tGyZh7x5L3LmjNXRSHJywvNr/KSau3E6KZWWJE1mnuNO8cma1GYgXft7G4WCBQsm+gymBpa3t/ddH5Oa+D6p1duKP5ZaAfiRI0cydOjQhK8vX76Mv78/vr6+ycYoklXi4uKw2+34+vri5ub0KmCRLKHxKq5E41VcicaruJKMHK+vvgovvwyzZ+fnqafK8vjj8NprdipUyJhYM9Ply7BsmY2QEDt+fn5WhyMpyAnPr45OQHI6KeXIMjYrlC9fHoDQ0NBkj0dHRxMREYGPj09CAsvb25vChQsTGRlJaGhosgmf+PPFnz818UsSw8PDiY6OTjbxdP78+UR9k+Ph4YGHh0eSdjc3N5cdmJJzxI9DjUVxBRqv4ko0XsWVaLyKK8nI8Vq4sElOPf88TJ0KderY6NcPJkyAUqXSH2tmWbQIGjc28Ur25urPr47G7ZrfXSr8/f3x8PDg4sWLySam9u3bB0CNGjUStdesWTPR8dvdvHmTX375BQ8PD/z9/e8ag4+PT0Kyaf/+/UmOh4aGcvHiRXx9fSlcuPBdzyciIiIiIiLZT/Hipmj4zz/D1avg7w///jdkx03WY2Lg7bdh3DirIxH5R45LSnl6etK8eXMAVq1aleR4fFtAQECi9kcffRSA1atXJ3lMcHAw169fp0WLFuTPn9+hOOLPn1wMQUFBALRr186hc4mIiIiIiEj25esL770Hu3bBkSNQqRLMnAl/V4HJFj78ECpXNnWxRLILp5NS586dY/78+WzevDnVfiEhIcyfPz9huVpWiK/DNH36dE6cOJHQvmvXLhYvXoy3tzd9+/ZN9Jh+/frh7e3N+vXrWbNmTUL7n3/+ycSJExOd93Z169albt26Sb6/IUOG4O7uzvvvv88PP/yQ0H7ixAlmzJiBu7s7gwcPTv83KyIiIiIiItlClSrw+eewcSN8/TXcd59ZMnfrlrVx3boF06fD2LHWxiFyJ6eTUkuWLGHMmDGEh4en2i8yMpIxY8awdOlSZy/Fxo0badGiRcIHwI0bNxK1bdy4MaF/q1atGDJkCH/99ReNGjWie/fudOnShbZt23Lz5k0WLFhA0aJFE12jaNGiLFiwADc3N3r27Em7du3o1asXdevW5ddff2Xw4MG0bNkySWzHjh3j2LFj3LyZeMcFf39/Jk+eTExMDG3atKFLly50796dhg0b8tdffzF58mSHlgKKiIiIiIiIa3nwQZOUWrrUzKCqXt0kq+LirInniy+gYEHQYh3JbpwudP7111+TL18+OnbsmGq/Dh06kC9fPr766ivGOpmWvXDhArt3707UZrfbE7VduHAh0fEZM2ZQq1YtFi5cSEhICHnz5qVFixaMGTOGxo0bJ3udzp078/XXX/Pmm2+ye/dubty4wf33389zzz1Hnz590hz30KFDqVSpErNnz2bHjh2AmVk1fPhw2rdvn+bziYiIiIiIiOto1Qp27oTVq00R9GnTYMoUaNsWbFlUa9xuN8XYx47NumuKOMoWHR1td+aBFSpUwMfHJ9nC4HeqU6cOUVFRnDx50plLyd+ioqIoU6YMkZGRye4QKJJV4uLiOHPmDH5+fi67G4TkHhqv4ko0XsWVaLyKK8kO4zU2Fj76yOzaV7GiSRQ1bJj51w0OhqFDTa2rPE5PS5GslB3Ga3pFRUVRuHBhwsLCUs1fOP3dxV/AEYULFyYiIsLZS4mIiIiIiIi4NHd36NcPjh2Dzp2hY0fz+eefM/e6U6fC6NFKSEn25HRSqlixYpw6dequ/ex2O6dOncLHx8fZS4mIiIiIiIjkCB4eMGwY/Por1KtndsPr2xdOn874a23fDidOmPOLZEdOJ6UeeOABwsPD+eKLL1Ltt2rVKi5dukS9evWcvZSIiIiIiIhIjlKoELzyChw/DsWKQa1aJln1558Zd42pU+Gll0wiTCQ7cjop1adPH+x2Oy+++CKbNm1Ktk9ISAhDhw7FZrM5VShcREREREREJCcrUQLefhsOHYIrV8Df3ySrIiPTd94DB2DHDhg0KGPiFMkMTq8qfeyxx+jUqRNr1qyhc+fO1KtXj4YNG+Lj40NERAQ7d+7kp59+wm6306FDh7vu0iciIiIiIiKSW/n5wfvvw8iR8O9/Q6VKMG4cDBkCnp5pP9+bb8Lzz5sZWSLZVbpKnS1evJjhw4ezbNky9uzZw08//ZRwzG63Y7PZ6N27N7NmzUp3oCIiIiIiIiI5XdWqsHIl7N4N48fDrFnw2mumSLqjxcpPnoR160zdKpHsLF1JKQ8PDxYsWMDQoUNZvXo1P//8M1FRUXh7e1OjRg06depEtWrVMipWERERERERkVzhoYfgm29g0yYzY2rGDJg0Cbp2Bbe7FOKZMQP69zdLA0WyswzZFLJatWpKPomIiIiIiIhksNatYdcuWL0aJkwwy/KmTIE2bcBm+6dfbCxs2waHD8OHH8LPP1sWsojDnC50LiIiIiIiIiKZz2aDLl1M8fLnn4dnn/0nWQUQFAQVKkDLlqYG1bVr0KyZaRfJzpSUEhEREREREXEBefKYZXnHjkGnTvD449CgAXTrBufOJe4bGmralZiS7ExJKREREREREREX4uEBw4aZ5NSRI2C3J+0T3zZ8uFnaJ5IdKSklIiIiIiIi4oL27YOoqJSP2+1w9qypNSWSHSkpJSIiIiIiIuKCwsIytp9IVlNSSkRERERERMQFlSmTsf1EspqSUiIiIiIiIiIuqGlTKF/e7M6XHJsNfH1NP5HsSEkpERERERERERfk7g5z5ph/35mYiv969mzTTyQ7UlJKRERERERExEUFBsLKlVCuXOL28uVNe2CgNXGJOCKP1QGIiIiIiIiIiPMCA6FTJ7PLXliYqSHVtKlmSEn2p6SUiIiIiIiIiItzd4cWLayOQiRttHxPRERERERERESynJJSIiIiIiIiIiKS5ZSUEhERERERERGRLKeklIiIiIiIiIiIZDklpUREREREREREJMspKSUiIiIiIiIiIlkuj9UBiOPsdjsAUVFRFkciuV1cXByXL18mKioKNzfltiV703gVV6LxKq5E41VcicaruJKcMF7j8xbxeYyUKCnlQq5cuQKAr6+vxZGIiIiIiIiIiKTuypUrFC5cOMXjtujo6NTTVpJtxMXFERYWRsGCBbHZbFaHI7nY5cuX8ff359ixYxQqVMjqcERSpfEqrkTjVVyJxqu4Eo1XcSU5Ybza7XauXLlCmTJlUp3tpZlSLsTNzY1y5cpZHYZIgkKFCuHt7W11GCIO0XgVV6LxKq5E41VcicaruBJXH6+pzZCK55qLE0VERERERERExKUpKSUiIiIiIiIiIllOSSkRSTMPDw/Gjx+Ph4eH1aGI3JXGq7gSjVdxJRqv4ko0XsWV5KbxqkLnIiIiIiIiIiKS5TRTSkREREREREREspySUiIiIiIiIiIikuWUlBIRh8ydO5cePXpQq1YtypQpQ5EiRahSpQrPPvssv/zyi9XhiSTr999/Z/To0dSuXZtixYpRvnx5Hn74YSZMmGB1aCKJ/PDDDzz55JP4+flRtGhRateuzeuvv87Vq1etDk1yob179zJz5kx69OhB5cqV8fLyomjRosn2jYuLY/v27UyYMIHmzZtzzz33UKRIEWrUqMGLL77I6dOnszZ4yXXSMl4BJk+ejJeXV4of//73v7Mweslt0jpeAc6fP8+wYcOoXr06RYoUoWTJkjRu3JhZs2YRExOTRZFnHtWUEhGH+Pn5ER0dTY0aNShbtiwAhw8f5vjx4+TLl48VK1bw6KOPWhylyD927dpFYGAgERERVK1alWrVqnH58mWOHDlCaGgoUVFRVocoAsCKFSt47rnniI2NpW7duvj6+vLTTz9x7tw5atasyTfffEOhQoWsDlNyke7du7N+/fpEbR4eHly6dClJ319//ZVatWoBULZsWerVq4ebmxs//vgj58+fp1ChQgQFBdG4ceMsiV1yn7SMVzBJqSlTptCoUSMqVqyY5HhAQACBgYGZEqtIWsfr8ePHeeSRR7h48SL33nsvtWvXJjo6mu+//54rV67QuHFjNmzYQN68ebMi/EyRx+oARMQ1fPrpp9StW5f8+fMnal+0aBHDhw/n+eef5+jRo7i7u1sUocg/wsLCCAwMJCYmhk8++YSOHTsmOv7jjz9aFJlIYqGhoTz//PPExsayYMEC+vTpA0BMTAz/+te/CAoKYuLEicyZM8fiSCU3qV+/PjVr1uSBBx6gXr16yb5wj2ez2XjkkUcYNWoUTZo0SWiPiYnhxRdfZNmyZTzzzDMcPHjQpV80SfaVlvF6u759+9K7d+9Mjk4ksbSO11deeYWLFy8yaNAgpk+fnvBa688//6RNmzbs2LGDTz75JOH+wRVpppSIpFutWrX49ddf+fHHH6latarV4Yjwr3/9i08++YS33nqLQYMGWR2OSIrefPNN3njjDVq1asW6desSHbtw4QLVqlXj5s2b/PrrrxQrVsyiKCW38/LySvWd/JRcv36dihUrEhkZycaNG2natGkmRSjyj7uN1/iZUu+++66SUmK5u41XPz8//vrrL06ePEmpUqUSHZs3bx5jxozh2WefZfbs2VkQbeZQTSkRSbf4jH2+fPksjkQEwsPDCQoKonDhwvTr18/qcERStXfvXgCaNWuW5FiJEiWoUqUKN2/e5Kuvvsrq0ETSLX/+/FSuXBkwM1hFRCRtHHl9VaRIkSyIJPNo+Z6IpMvy5cs5duwY9913H/fee6/V4Yiwc+dOYmJiaNmyJXnz5mXVqlXs2LGDW7du4e/vT2BgYJJ3mkSsEh0dDYCPj0+yx+NvNA8ePJhVIYlkmNjYWM6cOQOg513JdrZu3cqBAweIiYmhXLlytG3blrp161odlkgirVq1Yvny5cyYMYPp06fj5mbmFf35558sWrSIPHny0L17d4ujTB8lpUQkTWbNmsXhw4e5evX/27vjmCjrB47jnyNFETMGCEIBAaOCrMjpEBlbOi2q2UBYIdCEqKZLhpnMVpaLVbPSUmdhrLQyJYM0JQuQ5hA0gfLUCJ1kikgykZTwfixA7/cHg3ZBBan3HPp+bfcH3+d4+HB7xrjPfb/f5386cuSIDh8+LB8fH61fv773jyRgpJ67QXp5eWnGjBmqrKy0Ob506VKtXbuWTUzhEDw9PSWp9437XzU0NPzjccCR5efnq7m5WZ6enpo8ebLRcQAbeXl5Nl9nZ2crNjZW77//vkaPHm1QKsDWK6+8ov379ysnJ0dFRUUKDw+XxWLR3r175e7urs2bN+uOO+4wOuZl4R0kgEEpLS3Vxo0btXXrVh0+fFi33HKL1q9fzydLcBjnz5+XJG3atEk1NTV67733VF9fr9raWmVkZMhisSg9PZ2ZJ3AIPXvs5Ofnq6Ojw+ZYVVWVjh49Kklqa2uzezbgcpw6dUqLFy+WJL300ksaMWKEwYmAbkFBQXr99df1/fff68yZMzp69KjWrVsnX19fffnll3ryySeNjgj08vHxUXFxsaZNm6bjx49r69atKikpkcViUXR09JAvpCRKKQCDtGPHDlksFjU2NqqkpEQhISGKiYnRm2++aXQ0QFL3chFJ6urq0rJlyzRnzhx5enoqICBAy5YtU1xcnDo6OvTOO+8YnBTovjW0n5+fGhoa9Oijj6q2tlZtbW0qKSlRSkqKhg3rntTOTFQMJRaLRYmJiTp79qxmzpzJm3w4lNmzZyszM1OhoaFydXXVzTffrMcee0y7d++Wh4eHCgsLtW/fPqNjApK6l+9HRkbq559/1ueff67GxkbV1dXpjTfe0JYtWzR16lT98ssvRse8LPyHA+A/cXNzU1RUlLZu3ap7771X2dnZ+uGHH4yOBejGG2+U1P0mPjk5uc/xnjvtlJeX2zUX0B9XV1cVFBTIz89PO3fu1KRJkzRu3DjFxcXJyclJGRkZkv5+zynA0XR2diopKUlms1lTpkzR+vXrjY4EDIiPj49SUlIkda8MAIzW2dmpxx9/XKdPn1ZeXp4efvhhubm5ydfXV88884xefvllnTlzRtnZ2UZHvSyUUgAuy/DhwxUfHy+r1aqvv/7a6DiA/P39JXVvqtvfcpGAgABJUnNzs11zAX9n/PjxMpvNys3N1bx58/TUU09p9erVqq6u7n1OWFiYgQmBgbl06ZLS09NVWlqqu+66S/n5+XJxcTE6FjBgPXeLbGpqMjgJ0L2Mv66uToGBgQoPD+9zPD4+XtLQ/6CVjc4BXDYPDw9J0tmzZw1OAkj33HOPpO69paxWq0wmk83x3377TZLYxBQOxcXFRcnJyX1m9+3atUvSn3tPAY5swYIF+uKLLxQSEqLt27czww9Dzrlz5yR1z2IFjNbY2Cjpz1UAf9Uz3nPdDlXMlAJw2SoqKiRJgYGBBicBumed3HrrrWpvb7eZadKj59OknvIKcFTl5eU6cOCAQkNDFRkZaXQc4B8tXbpUH374ofz8/FRYWCgvLy+jIwGDYrVaVVhYKEncwAcOwdvbW5JUV1fX7w1PerZO6VkFMFRRSgH4V3v27FFBQYG6urpsxjs7O5WTk6O8vDy5uLgoISHBoISArYULF0qSFi1aZDODz2w2a/Xq1ZLExrtwGAcPHuzz99VsNistLU0mk0krVqwwKBkwMKtXr9by5cvl7e2tr776Sn5+fkZHAvp19uxZbdy4UX/88YfN+IULF5SZmanq6mp5e3vrkUceMSgh8KeIiAiNHTtWFotFCxcutLluT58+3XuH09jYWIMSXhkmi8ViNToEAMe2YcMGzZ07V56engoPD5e7u7taWlr0008/qampSSNHjlRubm7vumbAaJcuXdKcOXO0ZcsWubu7KyIiQhaLRfv27VNHR4fS0tK0Zs0ao2MCkqSYmBgdPnxYd999tzw8PHTy5ElVV1fLyclJK1euVFpamtERcZ0pKirSsmXLer+urq6WyWTSxIkTe8eef/55xcTE6ODBg4qKipLValVERETvnjx/lZqaqilTplz17Lj+DOZ6ra+vV1hYmMaMGaPbb79dfn5+On/+vA4ePKiWlha5ubmpoKCA2am4agZzvUpSYWGhUlJS1NXVJV9fX02YMEHt7e2qqqpSW1ubwsPDVVxcPKS3pWBPKQD/Kjo6WllZWaqoqFBNTY1aWlrk7Owsf39/xcXFad68eQoODjY6JtDLyclJH3/8saKjo/XRRx+prKxMJpNJEyZMUHp6upKSkoyOCPRKTEzUZ599pkOHDqm1tVWenp5KSEjQggULWGYKQzQ3N/dZ/my1Wm3Gem4W0draKqu1+zPuyspKVVZW9nvO6OhoSilcFYO5Xt3d3bVw4UJVV1fr2LFjOnTokG644QYFBAQoOTlZGRkZ8vX1tWt+XF8Gc71K0syZM1VWVqZVq1Zpz549Ki4ulrOzs4KDgzVr1izNnz9/yN9QgplSAAAAAAAAsDv2lAIAAAAAAIDdUUoBAAAAAADA7iilAAAAAAAAYHeUUgAAAAAAALA7SikAAAAAAADYHaUUAAAAAAAA7I5SCgAAAAAAAHZHKQUAAAAAAAC7o5QCAAAAAACA3VFKAQAADHGvvfaaXF1d9fTTTxsdBQAAYMCGGR0AAAAA/2zNmjVqbW1VSkqKAgICjI4DAABwRVBKAQAAOLh3331XJ0+eVHR0dL+llIeHh2677TaNGzfOgHQAAAD/DaUUAADAEDd37lzNnTvX6BgAAACDwp5SAAAAAAAAsDtKKQAAAAe1YcMGubq66uTJk5KkBx98UK6urr2Pno3N/2mj857n1tfXy2w2a/bs2QoICNDYsWMVFRWlzZs39z63ra1N2dnZCg8Pl4eHh4KCgpSZmalz5879bUar1ar8/HzFxsYqICBAbm5uCgwMVGJioioqKq7wKwIAAK4llFIAAAAOysvLS5GRkRoxYoQkKSwsTJGRkb2PkJCQAZ+rpKRE06ZN0+7du+Xn56dRo0bpwIEDeuKJJ5STk6OWlhZNnTpVb731loYPHy5/f381Nzfrgw8+0MyZM9XZ2dnnnBcuXNCsWbOUmpqqnTt39mbs7OxUYWGhYmJitHLlyivyWgAAgGsPpRQAAICDeuCBB1RaWipvb29J0ooVK1RaWtr7yMrKGvC5srKy9Nxzz+n48eOqqKjQiRMntHjxYklSdna20tLSNHr0aNXU1Ki6ulpms1m7du3STTfdJLPZrLy8vD7nnD9/vkpKShQaGqpvv/1W9fX12rt3r06dOqXc3Fy5uLhoyZIlKi8vvzIvCAAAuKZQSgEAAFwH7rvvPi1ZskTOzs6SJJPJpBdeeEE+Pj76/fffVVFRoU8++cTm7n4TJ05UamqqJOmbb76xOd/+/fuVn5+vMWPGaMuWLZo8ebLN8eTkZL344ouyWq16++23r+4vBwAAhiRKKQAAgOtAenp6n7Fhw4Zp/PjxkqQZM2bI39+/z3MmTJggSTp27JjNeEFBgSTp/vvv7/f7JCkuLk6SVF5erosXL/738AAA4Jo0zOgAAAAAuPqCg4P7HR87duyAjlssFpvxH3/8UZJUWVmp6dOn9/u9VqtVktTe3q6WlhZ5eXkNPjgAALhmUUoBAABcB1xdXfsdN5lMAzp+6dIlm/Hz589LkhoaGtTQ0PCvP7+9vX2gUQEAwHWCUgoAAACDNnr0aEnSq6++qmeffdbgNAAAYChiTykAAAAH1zNbyZHceeedkqTvvvvO4CQAAGCoopQCAABwcKNGjZLkWEvg4uPjJUlFRUUym80GpwEAAEMRpRQAAICDCwoKkiSVlZUZnORPkZGRSkhI0MWLFxUXF6dt27b12Xfq9OnTys3N1fLlyw1KCQAAHBl7SgEAADi4pKQk7dixQ6tWrdL27dvl6+srJycnTZ8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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(12,4),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "fig.subplots_adjust(wspace=0.15)\n",
    "ax.plot(qhcpi, marker='o', ls='-', lw=.75, color=\"blue\")\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(3))\n",
    "ax.set_ylabel(\"cpi of QingHai Province\", fontsize=17)\n",
    "ax.set_xlabel(\"time\", fontsize=17)\n",
    "ax.grid(alpha=0.4)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d7c1741",
   "metadata": {},
   "source": [
    "## stability test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "faf9fcb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF value: 2.419970292671094\tpvalue: 0.9990194000417\t\n",
      "ADF value: -2.5830562854245374\tpvalue: 0.28775293197027485\t\n",
      "ADF value: -3.2974570470039835\tpvalue: 0.17313789759647785\t\n",
      "ADF value: -0.0\tpvalue: 0.6842796360469544\t\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "resc = adfuller(qhcpi,regression='c',autolag='BIC',regresults=False)\n",
    "resct = adfuller(qhcpi,regression='ct',autolag='BIC',regresults=False)\n",
    "resctt = adfuller(qhcpi,regression='ctt',autolag='BIC',regresults=False)\n",
    "resnc = adfuller(qhcpi,regression='n',autolag='BIC',regresults=False)\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resc[0],resc[1]))\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resct[0],resct[1]))\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resctt[0],resctt[1]))\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resnc[0],resnc[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e937ce7-be23-4043-b40b-3d6fab17b548",
   "metadata": {},
   "source": [
    "As we can see here time series of qinghai CPI is not stationary, yet we will put this issue aside, for difference operation please check chapter 4 in later contents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3050a50b",
   "metadata": {},
   "source": [
    "## white noise test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "222d0219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "      <th>bp_stat</th>\n",
       "      <th>bp_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.530496</td>\n",
       "      <td>0.016509</td>\n",
       "      <td>12.442823</td>\n",
       "      <td>0.052788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>35.800803</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>19.734543</td>\n",
       "      <td>0.072277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lb_stat  lb_pvalue    bp_stat  bp_pvalue\n",
       "6   15.530496   0.016509  12.442823   0.052788\n",
       "12  35.800803   0.000349  19.734543   0.072277"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "acorr_ljungbox(qhcpi, lags=[6,12], boxpierce=True, return_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e167d295",
   "metadata": {},
   "source": [
    "# Model selection\n",
    "## plot acf & pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8b9ef90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.68387999,  0.20725119, -0.20159499, -0.31144883,\n",
       "       -0.2072726 ,  0.00449682,  0.18576131,  0.23531499,  0.05646715,\n",
       "       -0.19076899, -0.36719405, -0.37523631, -0.20410342, -0.05235272,\n",
       "        0.02610906,  0.01272568, -0.00203427])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "plot_acf(qhcpi)\n",
    "acf(qhcpi,nlags=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28f9e090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.72410822, -0.61214033, -0.23816897,  0.34595128,\n",
       "       -0.04196853,  0.09690395,  0.26160346, -0.149471  ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(qhcpi,lags=8)\n",
    "pacf(qhcpi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0ff7b7f",
   "metadata": {},
   "source": [
    "## ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb4cf682",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# define auto model picking process\n",
    "def auto_arma(x, n=10, crit='bic'):\n",
    "    pmax = int(len(x)/n)\n",
    "    qmax = int(len(x)/n)\n",
    "    # crit_ is a 2D matrix storing bic|aic result, \n",
    "    # for each row being q_\n",
    "    crit_ = []\n",
    "    for p in range(pmax+1):\n",
    "        q_ = []\n",
    "        for q in range(qmax+1):\n",
    "            try:\n",
    "                if(crit == 'bic'):\n",
    "                    q_.append(ARIMA(x, order=(p,0,q)).fit().bic)\n",
    "                else:\n",
    "                    q_.append(ARIMA(x, order=(p,0,q)).fit().aic)\n",
    "            except:\n",
    "                q_.append(None)\n",
    "        crit_.append(q_)\n",
    "    crit_ = pd.DataFrame(crit_)\n",
    "    # find p,q with minimum bic|aic value\n",
    "    p,q = crit_.stack().astype('float64').idxmin()\n",
    "    print(\"minimum p,q are: %s,%s\"%(p,q))\n",
    "    model = ARIMA(x, order=(p,0,q)).fit()\n",
    "    return model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd4bee5",
   "metadata": {},
   "source": [
    "### AR(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a525c66e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum p,q are: 3,0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>QHCPI</td>      <th>  No. Observations:  </th>   <td>18</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(3, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-10.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 07 Jun 2024</td> <th>  AIC                </th> <td>31.153</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:28:46</td>     <th>  BIC                </th> <td>35.605</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>31.767</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> - 18</td>      <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>  101.5381</td> <td>    0.184</td> <td>  552.629</td> <td> 0.000</td> <td>  101.178</td> <td>  101.898</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.8002</td> <td>    0.241</td> <td>    3.325</td> <td> 0.001</td> <td>    0.329</td> <td>    1.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>   -0.0364</td> <td>    0.370</td> <td>   -0.098</td> <td> 0.922</td> <td>   -0.762</td> <td>    0.689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.4833</td> <td>    0.349</td> <td>   -1.386</td> <td> 0.166</td> <td>   -1.167</td> <td>    0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1681</td> <td>    0.071</td> <td>    2.382</td> <td> 0.017</td> <td>    0.030</td> <td>    0.306</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.09</td> <th>  Jarque-Bera (JB):  </th> <td>5.71</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.77</td> <th>  Prob(JB):          </th> <td>0.06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>5.92</td> <th>  Skew:              </th> <td>-1.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.05</td> <th>  Kurtosis:          </th> <td>4.49</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &      QHCPI       & \\textbf{  No. Observations:  } &     18      \\\\\n",
       "\\textbf{Model:}                  &  ARIMA(3, 0, 0)  & \\textbf{  Log Likelihood     } &  -10.577    \\\\\n",
       "\\textbf{Date:}                   & Fri, 07 Jun 2024 & \\textbf{  AIC                } &   31.153    \\\\\n",
       "\\textbf{Time:}                   &     16:28:46     & \\textbf{  BIC                } &   35.605    \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } &   31.767    \\\\\n",
       "\\textbf{}                        &       - 18       & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &     101.5381  &        0.184     &   552.629  &         0.000        &      101.178    &      101.898     \\\\\n",
       "\\textbf{ar.L1}  &       0.8002  &        0.241     &     3.325  &         0.001        &        0.329    &        1.272     \\\\\n",
       "\\textbf{ar.L2}  &      -0.0364  &        0.370     &    -0.098  &         0.922        &       -0.762    &        0.689     \\\\\n",
       "\\textbf{ar.L3}  &      -0.4833  &        0.349     &    -1.386  &         0.166        &       -1.167    &        0.200     \\\\\n",
       "\\textbf{sigma2} &       0.1681  &        0.071     &     2.382  &         0.017        &        0.030    &        0.306     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.09 & \\textbf{  Jarque-Bera (JB):  } &  5.71  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.77 & \\textbf{  Prob(JB):          } &  0.06  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 5.92 & \\textbf{  Skew:              } & -1.16  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.05 & \\textbf{  Kurtosis:          } &  4.49  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  QHCPI   No. Observations:                   18\n",
       "Model:                 ARIMA(3, 0, 0)   Log Likelihood                 -10.577\n",
       "Date:                Fri, 07 Jun 2024   AIC                             31.153\n",
       "Time:                        16:28:46   BIC                             35.605\n",
       "Sample:                             0   HQIC                            31.767\n",
       "                                 - 18                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        101.5381      0.184    552.629      0.000     101.178     101.898\n",
       "ar.L1          0.8002      0.241      3.325      0.001       0.329       1.272\n",
       "ar.L2         -0.0364      0.370     -0.098      0.922      -0.762       0.689\n",
       "ar.L3         -0.4833      0.349     -1.386      0.166      -1.167       0.200\n",
       "sigma2         0.1681      0.071      2.382      0.017       0.030       0.306\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.09   Jarque-Bera (JB):                 5.71\n",
       "Prob(Q):                              0.77   Prob(JB):                         0.06\n",
       "Heteroskedasticity (H):               5.92   Skew:                            -1.16\n",
       "Prob(H) (two-sided):                  0.05   Kurtosis:                         4.49\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arma(qhcpi,5,crit='bic')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2734dba",
   "metadata": {},
   "source": [
    "giving that ARMA(3,0) which is:\n",
    "\n",
    "$$\n",
    "    x_t = 101.5381 + 0.8002x_{t-1} - 0.0364x_{t-2} - 4833x_{t-2} + \\epsilon_t\n",
    "$$\n",
    "\n",
    "follows that \n",
    "\n",
    "$$\n",
    "    \\epsilon_t \\sim \\mathcal{N}(0,0.1681)\n",
    "$$\n",
    "\n",
    "According to LB test, residual is WN, which means that model is complete"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e77d5e",
   "metadata": {},
   "source": [
    "### AR(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c7f28fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>QHCPI</td>      <th>  No. Observations:  </th>   <td>18</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-14.915</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 07 Jun 2024</td> <th>  AIC                </th> <td>35.830</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:28:46</td>     <th>  BIC                </th> <td>38.501</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>36.198</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> - 18</td>      <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>  101.4866</td> <td>    0.470</td> <td>  215.712</td> <td> 0.000</td> <td>  100.565</td> <td>  102.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.6534</td> <td>    0.159</td> <td>    4.112</td> <td> 0.000</td> <td>    0.342</td> <td>    0.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.2977</td> <td>    0.135</td> <td>    2.201</td> <td> 0.028</td> <td>    0.033</td> <td>    0.563</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>2.78</td> <th>  Jarque-Bera (JB):  </th> <td>2.04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.10</td> <th>  Prob(JB):          </th> <td>0.36</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>3.21</td> <th>  Skew:              </th> <td>-0.82</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.18</td> <th>  Kurtosis:          </th> <td>2.80</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &      QHCPI       & \\textbf{  No. Observations:  } &     18      \\\\\n",
       "\\textbf{Model:}                  &  ARIMA(1, 0, 0)  & \\textbf{  Log Likelihood     } &  -14.915    \\\\\n",
       "\\textbf{Date:}                   & Fri, 07 Jun 2024 & \\textbf{  AIC                } &   35.830    \\\\\n",
       "\\textbf{Time:}                   &     16:28:46     & \\textbf{  BIC                } &   38.501    \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } &   36.198    \\\\\n",
       "\\textbf{}                        &       - 18       & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &     101.4866  &        0.470     &   215.712  &         0.000        &      100.565    &      102.409     \\\\\n",
       "\\textbf{ar.L1}  &       0.6534  &        0.159     &     4.112  &         0.000        &        0.342    &        0.965     \\\\\n",
       "\\textbf{sigma2} &       0.2977  &        0.135     &     2.201  &         0.028        &        0.033    &        0.563     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 2.78 & \\textbf{  Jarque-Bera (JB):  } &  2.04  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.10 & \\textbf{  Prob(JB):          } &  0.36  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 3.21 & \\textbf{  Skew:              } & -0.82  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.18 & \\textbf{  Kurtosis:          } &  2.80  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  QHCPI   No. Observations:                   18\n",
       "Model:                 ARIMA(1, 0, 0)   Log Likelihood                 -14.915\n",
       "Date:                Fri, 07 Jun 2024   AIC                             35.830\n",
       "Time:                        16:28:46   BIC                             38.501\n",
       "Sample:                             0   HQIC                            36.198\n",
       "                                 - 18                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        101.4866      0.470    215.712      0.000     100.565     102.409\n",
       "ar.L1          0.6534      0.159      4.112      0.000       0.342       0.965\n",
       "sigma2         0.2977      0.135      2.201      0.028       0.033       0.563\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   2.78   Jarque-Bera (JB):                 2.04\n",
       "Prob(Q):                              0.10   Prob(JB):                         0.36\n",
       "Heteroskedasticity (H):               3.21   Skew:                            -0.82\n",
       "Prob(H) (two-sided):                  0.18   Kurtosis:                         2.80\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARIMA(qhcpi, order=(1,0,0)).fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98290f6",
   "metadata": {},
   "source": [
    "### AR(1,3)-sparse arma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a3fc3c",
   "metadata": {},
   "source": [
    "giving that ARMA(1,0) which is:\n",
    "\n",
    "$$\n",
    "    x_t = 101.4866 + 0.6534x_{t-1} + \\epsilon_t\n",
    "$$\n",
    "\n",
    "follows that \n",
    "\n",
    "$$\n",
    "    \\epsilon_t \\sim \\mathcal{N}(0,0.2977)\n",
    "$$\n",
    "\n",
    "while residual is somehow not WN, which proves that MA(1,0) is not completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "977a1f33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>QHCPI</td>        <th>  No. Observations:  </th>   <td>18</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>ARIMA([1, 3], 0, 0)</td> <th>  Log Likelihood     </th> <td>-10.584</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 07 Jun 2024</td>   <th>  AIC                </th> <td>29.169</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:28:46</td>       <th>  BIC                </th> <td>32.730</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                   <td>0</td>          <th>  HQIC               </th> <td>29.660</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td> - 18</td>        <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>opg</td>         <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>  101.5395</td> <td>    0.178</td> <td>  571.886</td> <td> 0.000</td> <td>  101.192</td> <td>  101.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.7802</td> <td>    0.165</td> <td>    4.741</td> <td> 0.000</td> <td>    0.458</td> <td>    1.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.5084</td> <td>    0.217</td> <td>   -2.342</td> <td> 0.019</td> <td>   -0.934</td> <td>   -0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1679</td> <td>    0.069</td> <td>    2.429</td> <td> 0.015</td> <td>    0.032</td> <td>    0.303</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.15</td> <th>  Jarque-Bera (JB):  </th> <td>5.48</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.70</td> <th>  Prob(JB):          </th> <td>0.06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>5.86</td> <th>  Skew:              </th> <td>-1.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.05</td> <th>  Kurtosis:          </th> <td>4.43</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &        QHCPI        & \\textbf{  No. Observations:  } &     18      \\\\\n",
       "\\textbf{Model:}                  & ARIMA([1, 3], 0, 0) & \\textbf{  Log Likelihood     } &  -10.584    \\\\\n",
       "\\textbf{Date:}                   &   Fri, 07 Jun 2024  & \\textbf{  AIC                } &   29.169    \\\\\n",
       "\\textbf{Time:}                   &       16:28:46      & \\textbf{  BIC                } &   32.730    \\\\\n",
       "\\textbf{Sample:}                 &          0          & \\textbf{  HQIC               } &   29.660    \\\\\n",
       "\\textbf{}                        &         - 18        & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &         opg         & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &     101.5395  &        0.178     &   571.886  &         0.000        &      101.192    &      101.888     \\\\\n",
       "\\textbf{ar.L1}  &       0.7802  &        0.165     &     4.741  &         0.000        &        0.458    &        1.103     \\\\\n",
       "\\textbf{ar.L3}  &      -0.5084  &        0.217     &    -2.342  &         0.019        &       -0.934    &       -0.083     \\\\\n",
       "\\textbf{sigma2} &       0.1679  &        0.069     &     2.429  &         0.015        &        0.032    &        0.303     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.15 & \\textbf{  Jarque-Bera (JB):  } &  5.48  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.70 & \\textbf{  Prob(JB):          } &  0.06  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 5.86 & \\textbf{  Skew:              } & -1.15  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.05 & \\textbf{  Kurtosis:          } &  4.43  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                SARIMAX Results                                \n",
       "===============================================================================\n",
       "Dep. Variable:                   QHCPI   No. Observations:                   18\n",
       "Model:             ARIMA([1, 3], 0, 0)   Log Likelihood                 -10.584\n",
       "Date:                 Fri, 07 Jun 2024   AIC                             29.169\n",
       "Time:                         16:28:46   BIC                             32.730\n",
       "Sample:                              0   HQIC                            29.660\n",
       "                                  - 18                                         \n",
       "Covariance Type:                   opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        101.5395      0.178    571.886      0.000     101.192     101.888\n",
       "ar.L1          0.7802      0.165      4.741      0.000       0.458       1.103\n",
       "ar.L3         -0.5084      0.217     -2.342      0.019      -0.934      -0.083\n",
       "sigma2         0.1679      0.069      2.429      0.015       0.032       0.303\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.15   Jarque-Bera (JB):                 5.48\n",
       "Prob(Q):                              0.70   Prob(JB):                         0.06\n",
       "Heteroskedasticity (H):               5.86   Skew:                            -1.15\n",
       "Prob(H) (two-sided):                  0.05   Kurtosis:                         4.43\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARIMA(qhcpi, order=((1,3),0,0)).fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22645dd9",
   "metadata": {},
   "source": [
    "# Prediction\n",
    "1. model ARIMA contains prediction, using <font color=maroon><b>ARIMA.get_prediction(start=, end=)</b></font>\n",
    "2. prediction contains confidence interval, using <font color=maroon><b>prediction.conf_int(alpha=)</b></font>\n",
    "3. prediction contains predicted mean, using <font color=maroon><b>prediction.predicted_mean</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd88bae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "qh_arima1 = ARIMA(qhcpi, order=(1,0,0)).fit()\n",
    "qh_arima2 = ARIMA(qhcpi, order=((1,3),0,0)).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c8c6021",
   "metadata": {},
   "source": [
    "## AR(1) prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a776f292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    lower QHCPI  upper QHCPI  lower QHCPI  upper QHCPI\n",
      "1    100.665423   102.063922   100.295263   102.434082\n",
      "2    100.992142   102.390640   100.621981   102.760800\n",
      "3    101.318860   102.717358   100.948700   103.087518\n",
      "4    101.514891   102.913389   101.144731   103.283549\n",
      "5    101.318860   102.717358   100.948700   103.087518\n",
      "6    100.861454   102.259953   100.491294   102.630113\n",
      "7    100.665423   102.063922   100.295263   102.434082\n",
      "8    100.469392   101.867891   100.099232   102.238051\n",
      "9    100.926798   102.325296   100.556638   102.695456\n",
      "10   101.188173   102.586671   100.818012   102.956831\n",
      "11   101.122829   102.521327   100.752669   102.891487\n",
      "12   101.057485   102.455984   100.687325   102.826144\n",
      "13   101.122829   102.521327   100.752669   102.891487\n",
      "14   100.142674   101.541172    99.772514   101.911332\n",
      "15    99.881299   101.279798    99.511139   101.649958\n",
      "16    99.685268   101.083767    99.315108   101.453927\n",
      "17   100.469392   101.867891   100.099232   102.238051\n",
      "18   100.861454   102.259953   100.491294   102.630113\n",
      "19   100.699729   102.370322   100.257549   102.812502\n",
      "20   100.631194   102.405299   100.161617   102.874876\n",
      "21   100.599030   102.415536   100.118230   102.896336\n",
      "22   100.582963   102.417274   100.097450   102.902787\n",
      "23   100.574506   102.416368   100.086995   102.903879\n",
      "24   100.569840   102.414916   100.081478   102.903278\n"
     ]
    }
   ],
   "source": [
    "qh_pred1 = qh_arima1.get_prediction(start=1, end=24)\n",
    "# 80% confidence interval\n",
    "qh_confint11 = qh_pred1.conf_int(alpha=0.20)\n",
    "# 95% confidence interval\n",
    "qh_confint12 = qh_pred1.conf_int(alpha=0.05)\n",
    "qh_confint1 = pd.concat([qh_confint11,qh_confint12],axis=1,ignore_index=False)\n",
    "print(qh_confint1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0466d59-6f30-4ae2-8005-4d7b8ba284f7",
   "metadata": {},
   "source": [
    "## AR(1.3) prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "04f72500",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    lower QHCPI  upper QHCPI  lower QHCPI  upper QHCPI\n",
      "1    100.271580   102.479085   100.271580   102.479085\n",
      "2    101.009100   102.874295   101.009100   102.874295\n",
      "3    101.451540   103.057740   101.451540   103.057740\n",
      "4    101.431430   103.037629   101.431430   103.037629\n",
      "5    100.943177   102.549377   100.943177   102.549377\n",
      "6    100.244504   101.850703   100.244504   101.850703\n",
      "7    100.162942   101.769141   100.162942   101.769141\n",
      "8    100.284725   101.890925   100.284725   101.890925\n",
      "9    100.983399   102.589599   100.983399   102.589599\n",
      "10   101.448002   103.054202   101.448002   103.054202\n",
      "11   101.014125   102.620324   101.014125   102.620324\n",
      "12   100.732756   102.338955   100.732756   102.338955\n",
      "13   100.861616   102.467815   100.861616   102.467815\n",
      "14    99.742098   101.348298    99.742098   101.348298\n",
      "15    99.379168   100.985367    99.379168   100.985367\n",
      "16    99.907641   101.513841    99.907641   101.513841\n",
      "17   101.047269   102.653469   101.047269   102.653469\n",
      "18   101.667920   103.274119   101.667920   103.274119\n",
      "19   101.521955   103.559214   101.521955   103.559214\n",
      "20   101.159963   103.419725   101.159963   103.419725\n",
      "21   100.521215   102.781612   100.521215   102.781612\n",
      "22    99.937845   102.298000    99.937845   102.298000\n",
      "23    99.542222   102.116060    99.542222   102.116060\n",
      "24    99.584546   102.272210    99.584546   102.272210\n"
     ]
    }
   ],
   "source": [
    "qh_pred2 = qh_arima2.get_prediction(start=1, end=24)\n",
    "# 80% confidence interval\n",
    "qh_confint21 = qh_pred2.conf_int(alpha=0.05)\n",
    "# 95% confidence interval\n",
    "qh_confint22 = qh_pred2.conf_int(alpha=0.05)\n",
    "qh_confint2 = pd.concat([qh_confint21,qh_confint22],axis=1,ignore_index=False)\n",
    "print(qh_confint2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24556810",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d10ddcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import ticker\n",
    "\n",
    "def plot_arima(x,arima,arima_pred):\n",
    "    confin1 = arima_pred.conf_int(alpha=0.20)\n",
    "    confin2 = arima_pred.conf_int(alpha=0.05)\n",
    "    fig, ax = plt.subplots(1,1,figsize=(12,4), dpi=150,\n",
    "                          facecolor='whitesmoke',\n",
    "                          edgecolor=\"gray\")\n",
    "    ax.plot(x[1:], color='red',label=\"real value\")\n",
    "    arima_pred.predicted_mean.plot(ax=ax, color='b', ls=\"--\",label=\"predict value\")\n",
    "    ax.fill_between(confin1.index, confin1.iloc[:,0],\n",
    "                   confin1.iloc[:,1], color='k', alpha=.1)\n",
    "    ax.fill_between(confin2.index, confin2.iloc[:,0],\n",
    "               confin2.iloc[:,1], color='k', alpha=.1)\n",
    "    ax.set_xlabel(\"time\", fontsize=17)\n",
    "    ax.legend(loc=\"best\", fontsize=12)\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(3))\n",
    "    ax.grid(alpha=0.4)\n",
    "    plt.xticks(fontsize=15)\n",
    "    plt.yticks(fontsize=15)\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87dd5a91-8ed8-49cf-ad71-6b6d145ac8f4",
   "metadata": {},
   "source": [
    "### AR(1) visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "12e7456a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_arima(qhcpi, qh_arima1, qh_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c2619f-9904-4acf-852e-25b951501cd2",
   "metadata": {},
   "source": [
    "### AR(1,3) visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0b943b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_arima(qhcpi, qh_arima2, qh_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7085955",
   "metadata": {},
   "source": [
    "## Residue White Noise\n",
    "### AR(1) residue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "345069e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "      <th>bp_stat</th>\n",
       "      <th>bp_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.094987</td>\n",
       "      <td>0.085485</td>\n",
       "      <td>8.327782</td>\n",
       "      <td>0.215059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27.671066</td>\n",
       "      <td>0.006178</td>\n",
       "      <td>14.797558</td>\n",
       "      <td>0.252695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lb_stat  lb_pvalue    bp_stat  bp_pvalue\n",
       "6   11.094987   0.085485   8.327782   0.215059\n",
       "12  27.671066   0.006178  14.797558   0.252695"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "qh_resd1 = qh_arima1.resid\n",
    "acorr_ljungbox(qh_resd1, lags=[6,12], boxpierce=True, return_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e203487-331f-4f05-ad07-d2519070ed07",
   "metadata": {},
   "source": [
    "### AR(1,3) residue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e158af0",
   "metadata": {},
   "source": [
    "which tells that residual of ARIMA(1,0,0) is somehow not closed to white noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a532b82f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "      <th>bp_stat</th>\n",
       "      <th>bp_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.089101</td>\n",
       "      <td>0.982008</td>\n",
       "      <td>0.750944</td>\n",
       "      <td>0.993325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.886649</td>\n",
       "      <td>0.865012</td>\n",
       "      <td>2.628589</td>\n",
       "      <td>0.997646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lb_stat  lb_pvalue   bp_stat  bp_pvalue\n",
       "6   1.089101   0.982008  0.750944   0.993325\n",
       "12  6.886649   0.865012  2.628589   0.997646"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qh_resd2 = qh_arima2.resid\n",
    "acorr_ljungbox(qh_resd2, lags=[6,12], boxpierce=True, return_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92606c62",
   "metadata": {},
   "source": [
    "which tells that residual of ARIMA((1,3),0,0) is a white noise, no useful information remained after processed with this ARIMA model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6244b001-2837-4a57-a1ab-86396b75b6a2",
   "metadata": {},
   "source": [
    "# d value of ARIMA\n",
    "In this chapter we will show difference (specifically 1-order difference with d=1) operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "f17f0e4c-497d-4839-88c7-4b17b49ae5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CPI in QingHai province\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import warnings\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import ticker\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "    \n",
    "warnings.filterwarnings(\"ignore\")\n",
    "qhcpi = pd.read_csv('./data/cpi.csv', usecols=['Date','QHCPI'], index_col=0)\n",
    "\n",
    "qhcpi_1 = qhcpi.QHCPI.shift(1) - qhcpi.QHCPI\n",
    "qhcpi_1.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d7e8643-ccb8-4daa-9f36-35231387a1a8",
   "metadata": {},
   "source": [
    "## plot time series after difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "afab4177-dce0-4e29-8bb2-28c436492023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(12,4),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "fig.subplots_adjust(wspace=0.15)\n",
    "ax.plot(qhcpi_1, marker='o', ls='-', lw=.75, color=\"blue\")\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(3))\n",
    "ax.set_ylabel(\"cpi of QingHai Province\", fontsize=17)\n",
    "ax.set_xlabel(\"time\", fontsize=17)\n",
    "ax.grid(alpha=0.4)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a338f3fd-2cf4-488b-b888-97ecc79b4749",
   "metadata": {},
   "source": [
    "## stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b3b4f0ca-ca19-4725-a11f-85a94d0d63a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF value: -3.2177613785961308\tpvalue: 0.018979327078248273\t\n",
      "ADF value: -3.0795228324368065\tpvalue: 0.11118611407182466\t\n",
      "ADF value: -2.968690180719303\tpvalue: 0.310319098538581\t\n",
      "ADF value: 0.7200561832443981\tpvalue: 0.8706394798430639\t\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "resc = adfuller(qhcpi_1,regression='c',autolag='BIC',regresults=False)\n",
    "resct = adfuller(qhcpi_1,regression='ct',autolag='BIC',regresults=False)\n",
    "resctt = adfuller(qhcpi_1,regression='ctt',autolag='BIC',regresults=False)\n",
    "resnc = adfuller(qhcpi_1,regression='n',autolag='BIC',regresults=False)\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resc[0],resc[1]))\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resct[0],resct[1]))\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resctt[0],resctt[1]))\n",
    "print(\"ADF value: {}\\tpvalue: {}\\t\".format(resnc[0],resnc[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fae0bf-a419-4f96-ad0a-f4004c22b2ad",
   "metadata": {},
   "source": [
    "as we can see from above, with constant mean, first-order difference render this time series stable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "cce196af-712d-4cd7-b2fd-02392a14ce43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lb_stat</th>\n",
       "      <th>lb_pvalue</th>\n",
       "      <th>bp_stat</th>\n",
       "      <th>bp_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.155497</td>\n",
       "      <td>0.083688</td>\n",
       "      <td>8.089516</td>\n",
       "      <td>0.231618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28.220888</td>\n",
       "      <td>0.005135</td>\n",
       "      <td>14.385188</td>\n",
       "      <td>0.276790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lb_stat  lb_pvalue    bp_stat  bp_pvalue\n",
       "6   11.155497   0.083688   8.089516   0.231618\n",
       "12  28.220888   0.005135  14.385188   0.276790"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "acorr_ljungbox(qhcpi_1, lags=[6,12], boxpierce=True, return_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2836b0a-8dcb-46e2-afbf-cc20a63b0a60",
   "metadata": {},
   "source": [
    "which tells that first_order difference time series is not white noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e705dae-53b2-47c2-854f-7305d6b43a71",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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